Method and system for dynamically generating therapeutic content based on the psychological state of a user of a digital therapeutic system

ABSTRACT

An application user is granted access to one or more applications that provide the user with information and assistance. Through the one or more applications, the user is provided with interactive content, and data related to aspects of the user&#39;s interaction with the provided content is collected. The collected interaction data is analyzed to remotely identify and monitor changes or anomalies in the psychological state of the user. Upon identification of changes or anomalies in the user&#39;s psychological state, one or more actions are taken to assist the user.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/717,295, naming Simon Levy as inventor, filed on Dec. 17, 2019,entitled “METHOD AND SYSTEM FOR REMOTELY MONITORING THE PSYCHOLOGICALSTATE OF AN APPLICATION USER USING MACHINE LEARNING-BASED MODELS,” whichis hereby incorporated by reference in its entirety as if it were fullyset forth herein.

BACKGROUND

In recent years, digital applications have come to play an increasinglylarge role in the daily lives of billions of people all over the world.Currently, a vast number of applications are readily available to usersover a wide variety of technologies. These applications range greatly intype and purpose, providing users with information and services such asproductivity tools, educational materials, and entertainment options. Astechnology advances, these applications are becoming more and moresophisticated in terms of the content and experiences they are able toprovide to users. For example, in addition to providing users withinformation and other types of static content, most modern applicationsare also able to provide users with a variety of interactive features,thereby allowing a user to select specific and/or customized contentbased on user input, user interactions, and user behavior. In this way,the benefits an application provides to a user can be customized to meetthe needs or desires of specific individuals.

Due to the increased use of these digital applications in the dailylives of users, many such applications are now being used to supplementor replace traditional human to human, i.e., in person, interactions.Further, it has become increasingly clear that this trend will continueto grow in the years to come. However, while these types of interactiveapplications can provide many beneficial features to users, currently,these applications still present a variety of limitations that need tobe addressed in order for this interactive technology to achieve itsfullest potential.

As a specific example, every day, millions of people are diagnosed witha wide variety of medical conditions, ranging greatly in type andseverity. A patient who has been diagnosed with a medical conditionoften experiences many hardships as a result of their diagnosis. Inaddition to physical effects, such as pain, discomfort, or loss ofmobility that may accompany the diagnosis, the hardships faced bypatients often further include financial difficulties resulting fromlost work, medical bills and the cost of treatments. Further still, apatient's diagnosis often negatively impacts their social interactionsand overall emotional well-being. The result is that many patientsexperience significant psychological distress as a result of theirdiagnosis, and often do not receive adequate support or treatment toalleviate this distress.

Often, when a patient is diagnosed with one or more medical conditions,the patient may be referred to additional health professionals forfurther care and treatment. For example, a patient may be referred to apsychologist, psychiatrist, counselor, or other mental healthprofessional. A patient may also be directed to one or more supportgroups to assist with any psychological distress that the patient may beexperiencing. While these traditional face-to-face options may begreatly beneficial to a patient, often times they do not provide enoughpsychological support. For example, when a patient is alone, at home, ornot otherwise engaged directly with their mental health professional orsupport group, they may experience a significant degree of one or morenegative emotional states, such as fear, anxiety, panic, and depression.Additionally, left unidentified and untreated, these negative emotionalstates often exacerbate the physical symptoms associated with apatient's diagnosis, which in turn can lead to greater psychologicaldistress.

Further, while some patients may recognize that they are, for example,anxious or distressed, and may actively seek out additional help, manypatients may experience these mental states without fully recognizingthem, and thus might not realize that they are in need of additionalhelp. Further still, many patients may feel embarrassed or ashamed abouttheir medical condition, which may discourage them from activelyreaching out for the help that they need. Consequently, the shortcomingsassociated with traditional psychological support and treatmentmechanisms can have significant and serious effects on a patient'soverall health, safety, and well-being.

Because current mechanisms for enabling mental health professionals tomonitor the psychological state of patients outside of a medical officeor support group setting are limited, the shortcomings associated withtraditional psychological support and treatment options presents atechnical problem, which requires a technical solution. As digitalapplications begin to replace human interactions, this problem becomeseven more pronounced. This is because people are increasingly relying onapplications to provide them with support and assistance in a widevariety of aspects of their daily lives, and the failure of traditionalsolutions to address these issues has the potential to lead tosignificant consequences for a large number of people.

What is needed, therefore, is a method and system to more accurately andremotely identify and monitor changes or anomalies in a patient'spsychological state in order to ensure that they receive adequate care,support, and treatment.

SUMMARY

Embodiments of the present disclosure provide an effective and efficienttechnical solution to the technical problem of accurately and remotelyidentifying and monitoring changes or anomalies in the psychologicalstate of a current user of one or more applications by monitoring thecurrent user's interaction with the various materials presented throughthe application interfaces of the one or more applications to obtaincurrent user interaction data. In one embodiment, the current userinteraction data is then compared to average user interaction dataassociated with average users to determine the current user's mentalstate and/or detect any anomalies in the current user's mental state. Inone embodiment, the current user's interaction data is compared withhistorical user interaction data associated with the current user todetermine the current user's mental state and/or detect any anomalies inthe current user's mental state. In one embodiment, the current user'sinteraction data is processed using one or more machine learning basedmental state prediction models to determine the current user's mentalstate and/or detect any anomalies in the current user's mental state.

Some embodiments of the present disclosure provide an effective andefficient technical solution to the technical problem of accurately andremotely identifying and monitoring changes or anomalies in thepsychological state of patients who have been diagnosed with one or moremedical conditions. In the disclosed embodiments, a patient diagnosedwith one or more medical conditions is prescribed access to a digitaltherapeutics application, which is designed to provide guided care tothe patient in a variety of ways.

In one embodiment, once a patient has been prescribed access to thedigital therapeutics application, the patient is free to access theapplication and utilize the tools provided by the application. Once thepatient accesses the application, the patient becomes a user of theapplication, and is provided with digital content through a userinterface of the application. The content provided to the user mayinclude information relating to one or more of the user's medicalconditions, as well as information relating to the user's current andpotential medications and/or treatments. The content provided to theuser may further include interactive content, such as questions orexercises related to the content, which are designed to encourage theuser to interact with a variety of multi-media materials through theapplication interface.

In one embodiment, the user's interaction with the various materialspresented through the application interface is monitored to obtain userinteraction data. User interaction data may include data such as theuser's speed of interaction with the materials presented, as well as theuser's comprehension of the materials presented. In various embodiments,the user's speed of interaction with the materials presented can bedetermined in a variety of ways such as, but not limited to, monitoringthe rate at which the user scrolls through text data, the rate at whichthe user clicks buttons that advance the user through the materials, orthe rate at which the user types textual strings in response toquestions or exercises provided by the application. In variousembodiments, other user data such as, but not limited to, user audiodata, user video data, and/or user biometric data such as eye scan ratedata, can be used to monitor the user's speed of interaction. The user'scomprehension of the materials presented can also be determined in avariety of ways, such as, but not limited to, intermittently presentingthe user with questions about the content while the user is engaged withthe application.

In some embodiments, the digital therapeutics application obtainsinteraction data from a plurality of application users and processesthis data to compute an average interaction speed and an averagecomprehension level, based on the interaction data associated with theplurality of users. In some embodiment, this information may be obtainedfrom third parties in a more general form, such as average reading speedfor a given demographic sector of the population. A particular user maythen be presented with interactive content, and the user's interactionspeed and comprehension level may be monitored and compared to theaverages to determine whether the particular user's interaction speedand/or comprehension level are within a predefined threshold of thecomputed averages. Upon a determination that the user's interactionspeed and/or comprehension level are outside of the predefinedthreshold, a prediction may be made, based on this determination, as tothe likely mental state of the application user, and additional actionmay be taken, as will be discussed in further detail below.

In some embodiments, once a user has been prescribed access to thedigital therapeutics application, a user profile is generated for thatparticular user. As the user interacts with the application content, theuser's interaction speed and comprehension level for each interactionsession are monitored and the resulting interaction data may be storedin a database associated with the user's profile. The user's interactiondata is then analyzed to determine the user's baseline interaction speedand comprehension level. The user's baseline may be periodically orcontinually updated over time. Each time the user accesses and interactswith the application, the resulting interaction data for the currentinteraction session may be compared to the user's baseline to determinewhether the user's interaction speed and/or comprehension level arewithin a predefined threshold of the user's baseline. Upon adetermination that the user's interaction speed and/or comprehensionlevel are outside of the predefined threshold, a prediction may be made,based on this determination, as to the likely mental state of theapplication user, and additional action may be taken, as will bediscussed in further detail below.

In some embodiments, multiple users are provided with information andinteractive content through a user interface of the digital therapeuticsapplication. Each user's interactions are monitored to collect userinteraction data, such as interaction speed and comprehension level.Additionally, mental state data is collected for each of the users, andthe mental state data is correlated with the user interaction data. Thecorrelated mental state and user interaction data is then utilized astraining data to generate one or more trained machine learning basedmental state prediction models.

Once one or more machine learning models have been generated, a currentuser may be provided with information and interactive content throughthe user interface of the application. The current user's interactionsare monitored to collect user interaction data, which is then providedto the one or more trained machine learning based mental stateprediction models, resulting in the generation of user mental stateprediction data for the current user.

In various embodiments, upon either identifying the likely mental stateof a user, or identifying a change or anomaly in the mental state of auser, additional actions may be taken by the digital therapeuticsapplication to assist the user, depending on the user's particularmental state or known medical conditions, and also depending upon adetermination of the severity of the change or anomaly. For example, ifa determination is made that a user who is normally calm is currently ina mildly anxious mental state, minor actions may be taken, such asadjusting the content and/or presentation of the information that isbeing provided to the user through the user interface. On the otherhand, if a user who is normally mildly anxious is currently in aseverely anxious, fearful, or depressed state, more extreme actions maybe taken, such as notifying one or more medical professionals associatedwith the user through a notification system of the application, or someother form of personal intervention from one or more medicalprofessionals associated with the user.

As a result of these and other disclosed features, which are discussedin more detail below, the disclosed embodiments provide an effective andefficient technical solution to the technical problem of remotelyidentifying and monitoring changes or anomalies in the psychologicalstate of application users, including users who have been diagnosed withone or more medical conditions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of a process for remotely identifying andmonitoring anomalies in the psychological state of application usersbased on analysis of average user interaction data and current userinteraction data in accordance with a first embodiment.

FIG. 2 is a block diagram of a production environment for remotelyidentifying and monitoring anomalies in the psychological state ofapplication users based on analysis of average user interaction data andcurrent user interaction data in accordance with a first embodiment.

FIG. 3 is a flow chart of a process for remotely identifying andmonitoring changes or anomalies in the psychological state ofapplication users based on historical user interaction data and currentuser interaction data in accordance with a second embodiment.

FIG. 4 is a block diagram of a production environment for remotelyidentifying and monitoring changes or anomalies in the psychologicalstate of application users based on historical user interaction data andcurrent user interaction data in accordance with a second embodiment.

FIG. 5 is a flow chart of a process for remotely identifying orpredicting the psychological state of application users based on machinelearning-based analysis and processing in accordance with a thirdembodiment.

FIG. 6 is a block diagram of a production environment for remotelyidentifying or predicting the psychological state of application usersbased on machine learning-based analysis and processing in accordancewith a third embodiment.

Common reference numerals are used throughout the figures and thedetailed description to indicate like elements. One skilled in the artwill readily recognize that the above figures are merely illustrativeexamples and that other architectures, modes of operation, orders ofoperation, and elements/functions can be provided and implementedwithout departing from the characteristics and features of theinvention, as set forth in the claims.

DETAILED DESCRIPTION

Embodiments will now be discussed with reference to the accompanyingfigures, which depict one or more exemplary embodiments. Embodiments maybe implemented in many different forms and should not be construed aslimited to the embodiments set forth herein, shown in the figures, ordescribed below. Rather, these exemplary embodiments are provided toallow a complete disclosure that conveys the principles of theinvention, as set forth in the claims, to those of skill in the art.

Embodiments of the present disclosure provide an effective and efficienttechnical solution to the technical problem of remotely identifying andmonitoring changes or anomalies in the psychological state ofapplication users. In the disclosed embodiments, a user is grantedaccess to one or more applications designed to provide the user withinformation and assistance in a variety of ways. Through the one or moreapplications, the user may be provided with interactive content, whichallows for the collection of data related to aspects of the user'sinteraction with the provided content. The collected interaction data isthen analyzed to identify and monitor changes or anomalies in thepsychological state of the user. Upon identification of changes oranomalies in the user's psychological state, one or more actions aretaken to assist the user.

FIG. 1 is a flow chart of a process 100 for remotely identifying andmonitoring anomalies in the psychological state of application usersbased on analysis of average user interaction data and current userinteraction data in accordance with a first embodiment.

Process 100 begins at BEGIN 102 and process flow proceeds to 104. At104, one or more users of an application are provided with a userinterface, which allows the one or more users to receive output from theapplication, as well as to provide input to the application.

In various embodiments, the application may be any type of applicationthat is capable of providing content/information to a user through auser interface, including, but not limited to, a desktop computingsystem application, a mobile computing system application, a virtualreality computing system application, an application provided by anInternet of Things (IoT) device, or any combination thereof. In variousembodiments, the user interface may include any combination of agraphical user interface, an audio-based user interface, a touch-baseduser interface, or any other type of user interface currently known tothose of skill in the art, or any other type of user interface that maybe developed after the time of filing.

In one embodiment, the application provided to the one or more users isa digital therapeutics application, which is designed to assist patientswho have been diagnosed with one or more medical conditions. As aspecific illustrative example, upon diagnosing a patient with one ormore medical conditions, a medical care professional may prescribe thepatient access to the digital therapeutics application. The digitaltherapeutics application may be accessed by the patient through any typeof computing system that is capable of providing a user interface to auser, as discussed above. Upon accessing the digital therapeuticsapplication, the patient then becomes a user of the application, and isprovided with a user interface, which enables the user to interact withthe digital therapeutics application.

In one embodiment, once one or more users of an application are providedwith a user interface at 104, process flow proceeds to 106. At 106, theone or more users are provided with information through the userinterface.

In various embodiments, the information provided to the one or moreusers through the user interface includes, but is not limited to,textual information, audio information, graphical information, imageinformation, video information, and/or any combination thereof. In oneembodiment, the information is provided to the one or more users in sucha way that allows the one or more users to interact with the informationprovided. For example, a user may be presented with information on thescreen of an electronic device, along with a variety of graphical userelements, which allow the user to scroll through the information, clickon buttons associated with the information, and/or enter textual stringsin response to the information. When the information is presented to auser on a device that includes a touch screen, the interaction mayinclude touch-based interactions and/or gesture recognition. In additionto textual inputs and touch or click-based inputs, in variousembodiments, the user may be able to interact with the informationthrough more advanced input mechanisms such as through audio input,video input, accelerometer input, voice recognition, facial recognitionor through a variety of physiological sensors. Examples of physiologicalsensors may include, but are not limited to, heart rate monitors, bloodpressure monitors, eye tracking monitors, or muscle activity monitors.

As one specific illustrative example, in one embodiment, once one ormore users of a digital therapeutics application are provided with auser interface, they may be provided with content-based information suchas, but not limited to, information related to medical history, currentor potential medical care providers, medical conditions, medications,nutritional supplements, advice or suggestions regarding diet and/orexercise, or any other type of information that may be consideredrelevant to the one or more users.

In one embodiment, the content-based information may be provided solelyin a text format, however in various other embodiments, a user may alsobe presented with images that accompany the text, for example, imagesthat depict one or more visual symptoms related to the user's medicalconditions. The user may further be presented with graphical content,such charts, graphs, digital simulations, or other visualization tools.As one illustrative example, a user might be presented with a chart orgraph that compares the user's symptoms with those of other patientsdiagnosed with the same or similar conditions. The user may further bepresented with audio and/or video information related to their medicalconditions. As additional illustrative examples, the user may beprovided with one or more instructional videos that guide the userthrough physical therapy exercises, or educational videos that informthe user about the history and/or science behind their medicalconditions. In various embodiments, the user may be presented with anycombination of the above types of content-based information, or anyother additional types of content that may be relevant to the particularuser.

In addition to the types of content-based information discussed above,another type of information that may be provided to the one or moreusers is aesthetics-based information. This type of information may notbe immediately recognized by a user, but it nevertheless plays animportant role in the way in which the user absorbs and reacts to thepresentation of the content-based information. This aesthetics-basedinformation is used to create the overall user experience that isprovided to a user by an application, and thus may also be referred toherein as user experience information, or user experience data. Examplesof user experience data include, but are not limited to, the colors andfonts used to present the content-based information to a user, thevarious shapes of the graphical user interface elements, the layout orordering of the content-based information presented to a user, and/orthe sound effects, music, or other audio elements that may accompany thepresentation of or interaction with the content-based information.

In one embodiment, once the one or more users are provided withinformation through the user interface at 106, process flow proceeds to108. At 108, the interactions of the one or more users with theinformation presented through the user interface are monitored andcollective user interaction data is generated.

The interactions of one or more users with the information presentedthrough the user interface may be monitored through collection of userinput data received through the user interface. The user input datacollected may include, but is not limited to, data associated withclick-stream input, textual input, touch input, gesture input, audioinput, image input, video input, accelerometer input, and/orphysiological input. In one embodiment, once the user input data iscollected from the one or more users, the user input data from each ofthe one or more users is processed and aggregated to generate collectiveuser interaction data.

As one illustrative example, in one embodiment, a digital therapeuticsapplication may be configured to monitor specific types of userinteraction data, in order to enable further data analysis andprocessing. In one embodiment, the digital therapeutics application maybe configured to monitor the speed at which one or more users interactwith the information provided. In one embodiment, the speed at which auser interacts with the information provided may be measured bycollecting clickstream data, which may include data such as how long auser spends engaging with various parts of the information contentpresented to the user.

For example, consider the situation where a user of a digitaltherapeutics application is presented with a lengthy article related toone or more of their medical conditions. In this example, the user wouldlikely need to fully scroll through the content to read the entirearticle. The time it takes for a user to scroll from the top of the textto the bottom of the text may be determined from the user input data,and this input data could then be used to generate user interaction datarepresenting the speed at which the user read, or interacted, with thearticle.

As a further example, a user of a digital therapeutics application maybe presented with a series of screens, where each screen may contain oneor more types of information related to the user's medical conditions.For instance, the first screen may include text and images, the secondscreen may include one or more graphical visualizations, and the thirdscreen may include an audio/video presentation, along with textualinformation. Each screen may have user interface elements, such asnavigation buttons, allowing the user to move forward and backwardsbetween the different screens. The time it takes the user to click ortouch from one screen to the next, or from the beginning to the end ofthe presentation may be determined from the user input data, and thisinput data could then also be used to generate user interaction datarepresenting the speed at which the user read, or interacted with, thepresentation.

Additionally, a user may be presented with a variety of questions orexercises requiring textual responses, and the frequency of the typingand deleting events could be used to generate user interaction datarepresenting the speed at which the user interacted with the exercisematerials.

In another embodiment, the digital therapeutics application may beconfigured to monitor one or more users' interactions with theinformation to determine the one or more users' level of comprehensionwith respect to that information. In one embodiment, the level ofcomprehension associated with a user and the information provided to theuser may be measured by periodically presenting the user with a varietyof prompts or questions designed to determine whether the user isengaged with and understanding the information being presented. Acomprehension level may then be calculated, for example, based on thepercentage of questions that the user answered correctly.

Further, in one embodiment, a user's level of comprehension may bedetermined based on the percentage of the provided information that theuser read or interacted with. For example, if a user begins reading anarticle, but the user input data indicates that the user never scrollsto the end of the article, it may be determined that the user has poorcomprehension of the information provided. Likewise, in the case where auser is presented with multiple screens of information, for example, tenscreens, if the user only navigates to two of the ten screens, then itmay be determined that the user has poor comprehension of theinformation provided.

It should be noted here, that the foregoing examples are given forillustrative purposes only, and are not intended to limit the scope ofthe invention as disclosed herein and as claimed below.

In one embodiment, once the interactions of the one or more users withthe information presented through the user interface are monitored andthe associated collective user interaction data is generated at 108,process flow proceeds to 110. In one embodiment, at 110, the collectiveuser interaction data is analyzed, and average user interaction data isgenerated.

As discussed above, in various embodiments, the collective userinteraction data may include, but is not limited to, data generatedbased on associated click-stream input, textual input, touch input,gesture input, audio input, input, video input, accelerometer input,and/or physiological input obtained through monitoring of theinteractions of one or more users with the information provided throughthe user interface.

In one embodiment, at 110, the collective user interaction data isanalyzed to determine averages across the one or more users with respectto individual types of user interaction data. For example, types of userinteraction data may include, but are not limited to, the number oftimes a user accesses the application, the length of time a user spendsengaging with the application, how long a user has had access to theapplication, the type of information that a user engages with the mostwhile using the application, whether or not a user utilizes advancedinput mechanisms, the type of input mechanisms most preferred by a user,the speed at which a user engages with the information presented throughthe application, and the level of comprehension a user has of theinformation presented through the application.

Consider the above described illustrative example, in which a digitaltherapeutics application is configured to monitor the speed at which oneor more users engage with the information presented through the userinterface, as well the level of comprehension one or more users have ofthe information presented through the user interface. In this specificillustrative example, at 110, the collective user interaction data wouldinclude data indicating the speed at which each of the one or more usersinteracts with the information presented, as well as data indicating thelevel of comprehension that each of the one or more users has withrespect to the information presented. Each of the one or more users mayhave multiple associated data points that form part of the collectiveuser interaction data. For example, one user may have a particularinteraction speed and/or comprehension level associated with aparticular piece of information, received on a particular day. The sameuser may have a different interaction speed and/or comprehension levelassociated with the same piece of information, received on a differentday, etc. Further, it may be considered desirable for the digitaltherapeutics application to group the collective user data based on usercharacteristics such as, but not limited to, age, gender, race, or typeof medical condition. Thus, the digital therapeutics application may beconfigured to consider a wide variety of factors when analyzing thecollective user interaction data to generate average user interactiondata.

As one simplified illustrative example, the digital therapeuticsapplication may be configured to analyze the collective user interactiondata to calculate an average speed of interaction with a particulararticle of information among all female users, aged 55-65, who have beendiagnosed with breast cancer. The application may further be configuredto calculate an average level of comprehension of video content amongall male users, aged 65-75, who have been diagnosed with Alzheimer'sdisease. It should be readily apparent from the above illustrativeexamples that a vast number of configuration options would be availableto determine averages among users of the application, and the specificconfigurations would depend upon the goals of the applicationadministrators. As such, it should again be noted here, that theforegoing examples are given for illustrative purposes only, and are notintended to limit the scope of the invention as disclosed herein.

In one embodiment, once the collective user interaction data is analyzedand average user interaction data is generated at 110, process flowproceeds to 112. In one embodiment, at 112, one or more threshold userinteraction differentials are defined and utilized to generate thresholduser interaction differential data.

In one embodiment, one or more threshold user interaction differentialsare defined, such that users whose user interaction data varies from theaverage user interaction data can be identified. In one embodiment, athreshold user interaction differential represents a maximum allowablevariation between a specific user's interaction data and the averageuser interaction data. In various embodiments, the threshold userinteraction differential may be defined in various ways, such as, butnot limited to, through application configuration options, or use of apredetermined standard.

Continuing the example of the digital therapeutics application, in oneembodiment, after generation of the average user interaction data, itmay be determined that the average level of comprehension of videocontent among male users, aged 65-75, who have been diagnosed withAlzheimer's disease is 50%, where 50% represents the percentage ofcomprehension questions related to video content that were correctlyanswered by the patients in this particular group. It may be decided byspecialists, or other health care professionals, that a 10% variance isrelatively common, and as such, patients in this group whose userinteraction data indicated a 40% comprehension level with respect tovideo content would not raise concerns. However, if the threshold userinteraction differential were defined at 20% variance, then patients inthis group whose user interaction data indicated a 29% comprehensionlevel with respect to video content would raise concerns, and furtheraction might be deemed appropriate, as will be discussed in furtherdetail below.

As already noted above, in various embodiments, a large number ofindividual possible averages may be generated during the generation ofthe average user interaction data at 110, depending on the variousgroupings of users and user interaction data types, and as such, itfollows from the preceding discussion that there could potentially be adifferent threshold user interaction differential associated with eachof the individual averages that form the average user interaction data.In one embodiment, this collection of threshold user interactiondifferentials is aggregated to generate threshold user interactiondifferential data.

In one embodiment, once one or more threshold user interactiondifferentials are defined and utilized to generate threshold userinteraction differential data at 112, process flow proceeds to 114. Inone embodiment, at 114, a current user of the application is providedwith information through the user interface of the application.

In contrast to operation 106 described above, where one or more usersare provided with information through the application user interface, at114, a single specific user is provided with information through theuser interface of the application, during a single current session ofusing the application. Therefore, the single specific user may hereafterbe referred to as the current user.

As described in detail above, with respect to information provided toone or more users, in various embodiments, the information provided tothe current user through the user interface includes, but is not limitedto, textual information, audio information, graphical information, imageinformation, video information, user experience information, and/or anycombination thereof. In one embodiment, the information is provided tothe current user in such a way that allows the current user to interactwith the information provided.

In one embodiment, once information is provided to a current user at114, process flow proceeds to 116. In contrast to operation 108described above, where one or more users' interactions with theinformation provided through the user interface are monitored togenerate collective user interaction data, at 116, the current user'sinteractions with the information provided through the user interfaceare monitored to generate current user interaction data.

As described in detail above, with respect to monitoring theinteractions of one or more users to generate collective userinteraction data, in various embodiments, the interactions of thecurrent user with the information presented through the user interfacemay be monitored through collection of user input data received throughthe user interface. The user input data collected may include, but isnot limited to, data associated with click-stream input, textual input,touch input, gesture input, audio input, image input, video input,accelerometer input, and/or physiological input. In one embodiment, oncethe user input data is collected from the current user, the user inputdata is processed and aggregated to generate current user interactiondata.

As also described in detail above, with respect to monitoring theinteractions of one or more users to generate collective userinteraction data, in various embodiments, the application may beconfigured to monitor specific types of current user interaction data,such as, but not limited to, the speed at which the current userinteracts with the information provided, and/or the current user's levelof comprehension with respect to the information provided. In oneembodiment, the speed at which the current user interacts with theinformation provided may be measured by collecting clickstream data,which may include data such as how long the current user spends engagingwith various parts of the information content presented to the currentuser through the user interface. In one embodiment, the level ofcomprehension associated with the current user and the informationprovided may be measured by periodically presenting the current userwith a variety of prompts or questions designed to determine whether thecurrent user is engaged with and understanding the information beingpresented. A comprehension level may then be calculated, for example,based on the percentage of questions that the current user answeredcorrectly. Further, in one embodiment, the current user's level ofcomprehension may be determined based on the percentage of the providedinformation that the current user read or interacted with.

In one embodiment, once the current user's interactions with theinformation provided through the user interface are monitored togenerate current user interaction data at 116, process flow proceeds to118. In one embodiment, at 118, the current user interaction data isanalyzed along with the average user interaction data, to generatecurrent user interaction differential data, which represents anydifferential between the current user interaction data and the averageuser interaction data.

In one embodiment, the current user interaction data is analyzed toextract the data that is most relevant to the type of user interactiondata the application has been configured to monitor. For example, if theapplication has been configured to monitor user interaction speed anduser comprehension level, then data related to the current user'sinteraction speed and the current user's comprehension level isextracted from the current user interaction data.

In one embodiment, once the relevant user interaction data has beenextracted from the current user interaction data, the average userinteraction data is analyzed to determine the data in the average userinteraction data that corresponds to the relevant user interaction data.The current user interaction data is then compared to the correspondingdata in the average user interaction data to determine whether there isany differential between the current user interaction data and thecorresponding data in the average user interaction data, and currentuser interaction differential data is generated, which represents anysuch differential between the current user interaction data and thecorresponding data in the average user interaction data.

Returning to the illustrative example of the digital therapeuticsapplication described above, if the current user is a female, aged 60,who has been diagnosed with breast cancer, and the relevant userinteraction data is data associated with speed of interaction, then theaverage user interaction data would be analyzed to extract the data thatprovides the average speed of interaction of females aged 55 to 65, whohave been diagnosed with breast cancer. If, for example, the currentuser interaction speed is measured to be 150 words per minute, and thecorresponding average interaction speed is 200 words per minute, thenthe differential between the current user interaction speed and thecorresponding average interaction speed would be 50 words per minute,and this value would be represented by the current user interactiondifferential data. In various embodiments, the current user interactiondifferential data includes differential data related to multiple typesof user interaction data. For example, the current user interactiondifferential data may include, but is not limited to, differential datarelated to current user speed of interaction, as well as differentialdata related to current user comprehension level.

As already noted several times above, the foregoing examples are givenfor illustrative purposes only, and are not intended to limit the scopeof the invention as disclosed herein and as claimed below. As oneexample, user interaction speed may be measured using any means ofmeasurement available, and should not be construed herein as limited toa measurement requiring words per minute.

In one embodiment, once the current user interaction data is analyzedalong with the average user interaction data to generate current userinteraction differential data at 118, process flow proceeds to 120. At120, the current user interaction differential data for one or moretypes of user interaction data is compared with the threshold userinteraction differential data corresponding to the same one or moretypes of user interaction data to determine whether one or more of thecurrent user interaction differentials is greater than the correspondingthreshold user interaction differentials.

For example, in one embodiment, the current user interactiondifferential associated with user interaction speed may be compared tothe threshold user interaction differential associated with userinteraction speed, and the current user interaction differentialassociated with user comprehension level may be compared to thethreshold user interaction differential associated with usercomprehension level. In this example, the comparison may yield thatnone, one, or both of the user interaction differentials is greater thantheir corresponding threshold user interaction differentials.

In one embodiment, once the current user interaction differential datais compared with the threshold user interaction differential data at120, process flow proceeds to 122. At 122, if one or more of the currentuser interaction differentials is greater than the correspondingthreshold user interaction differentials, it may be determined that thisis indicative of an anomaly in the psychological state of the user, andthis data may be utilized to arrive at one or more predictions regardingthe current user's mental state. Upon identifying the current user'smental state and/or identifying anomalies in the user's mental state,one or more actions may be taken.

In one embodiment, the actions to be taken may be determined based onthe severity of any anomaly. For example, if the anomaly is minor, thenactions might be taken to make minor adjustments to the informationcontent data and/or the user experience data that is presented to thecurrent user. On the other hand, if the anomaly is severe, then actionsmight be taken to make major adjustments to the information content dataand/or the user experience data that is presented to the current user.In one embodiment, adjustments to the information content data mayinclude adjustments such as, but not limited to, providing textualcontent that uses gentler language, providing audio content thatincludes quieter, more relaxing voices, sounds, or music, or providingimage/video content that is less realistic or less graphic. Adjustmentsto the user experience data may include adjustments such as, but notlimited to, changing the colors, fonts, shapes, presentation, and/orlayout of the information content data presented to the current user.

For example, in one embodiment, as discussed above, the application is adigital therapeutics application, and the current user is a patient whohas been diagnosed with a medical condition. Many patients experience agreat deal of anxiety related to their medical conditions. If an anomalyis detected in the psychological state of the current user, this mayindicate that the current user is experiencing a higher than normallevel of anxiety, and therefore may benefit from assistance, or fromadjustments designed to reduce the current user's anxiety level.

As one specific illustrative example, if a determination is made thatthe current user is slightly more anxious than a corresponding averageuser, minor actions may be taken to reduce the current user's anxietylevel, such as adjusting the content and/or presentation of theinformation that is being provided to the current user through the userinterface. As one simplified illustrative example, cool colors such asblue and violet are known to produce calming effects, and rounder,softer shapes are also associated with calming effects. So in thissituation, the user experience content data may be modified so that thecontent is presented to the user with a blue/violet color scheme, andthe graphical user elements may be changed to include rounder and softershapes. As another specific illustrative example, if a determination ismade that the current user is significantly more anxious than acorresponding average user, more extreme actions may be taken, such asnotifying one or more medical professionals associated with the userthrough a notification system of the application, or some other form ofpersonal intervention from one or more medical professionals associatedwith the current user.

In various embodiments several additional types of actions may beappropriate specifically when dealing with users who have been diagnosedwith a medical condition, such as, but not limited to: asking the userfor input and/or response data; alerting the user; alerting one or moreof the user's mental health or medical professionals; making notes in,adding data to, or highlighting the user's electronic file; making aspecialist referral; recommending support contacts to the user;prescribing additional appointments, treatments, actions, ormedications; calling emergency response or intervention professionals;notifying emergency contacts, relatives, or caregivers, etc.

In one embodiment, once one or more actions are taken based on thecurrent user interaction data at 122, process flow proceeds to END 124and the process 100 for remotely identifying and monitoring anomalies inthe psychological state of application users based on analysis ofaverage user interaction data and current user interaction data isexited to await new data and/or instructions.

FIG. 2 is a block diagram of a production environment 200 for remotelyidentifying and monitoring anomalies in the psychological state ofapplication users based on analysis of average user interaction data andcurrent user interaction data in accordance with a first embodiment.

In one embodiment, production environment 200 includes user computingenvironments 202, current user computing environment 206, and serviceprovider computing environment 210. User computing environments 202 andcurrent user computing environment 206 further comprise user computingsystems 204 and current user computing system 208, respectively. Thecomputing environments 202, 206, and 210 are communicatively coupled toeach other with one or more communication networks 216.

In one embodiment, service provider computing environment 210 includesprocessor 212, physical memory 214, and application environment 218.Processor 212 and physical memory 214 coordinate the operation andinteraction of the data and data processing modules associated withapplication environment 218. In one embodiment, application environment218 includes user interface 220, which is provided to user computingsystems 204 and user computing system 208 through the one or morecommunication networks 216.

In one embodiment, application environment 218 further includes userinteraction data generation module 226, collective user interaction dataanalysis module 232, threshold user interaction definition module 236,current user interaction data analysis module 242, differentialcomparator module 246, action determination module 248, and actionexecution module 250, each of which will be discussed in further detailbelow.

Additionally, in one embodiment, application environment 218 includesinformation content data 222, user experience data 224, collective userinteraction data 230, average user interaction data 234, threshold userinteraction differential data 238, current user interaction data 240,and current user interaction differential data 244, each of which willbe discussed in further detail below. In some embodiments, collectiveuser interaction data 230, average user interaction data 234, andcurrent user interaction data 240 may be stored in user database 228,which includes data associated with one or more users of applicationenvironment 218.

In one embodiment, user computing systems 204 of user computingenvironments 202, which are associated with one or more users ofapplication environment 218, are provided with a user interface 220,which allows the one or more users to receive output from theapplication environment 218, as well as to provide input to theapplication environment 218, through the one or more communicationnetworks 216.

As discussed above, in various embodiments, the application environment218 may be any type of application environment that is capable ofproviding a user interface and content/information to a user, including,but not limited to, a desktop computing system application, a mobilecomputing system application, a virtual reality computing systemapplication, an application provided by an Internet of Things (IoT)device, or any combination thereof. Further, in various embodiments, theuser interface 220 may include any combination of a graphical userinterface, an audio-based user interface, a touch-based user interface,or any other type of user interface currently known to those of skill inthe art, or any other type of user interface that may be developed afterthe time of filing.

In one embodiment, user computing systems 204 of user computingenvironments 202, which are associated with one or more users ofapplication environment 218, are provided with information content data222 and user experience data 224 through the user interface 220.

In various embodiments, the information content data 222 provided to theone or more users through the user interface 220 includes, but is notlimited to, textual information, audio information, graphicalinformation, image information, video information, and/or anycombination thereof. In one embodiment, the information content data 222is provided to the one or more users in such a way that allows the oneor more users to interact with the information content data 222.

In various embodiments, the user experience data 224 includes, but isnot limited to, colors and fonts used to present the information contentdata 222 to a user, the various shapes of graphical user interfaceelements, the layout or ordering of the information content data 222,and/or the sound effects, music, or other audio elements that mayaccompany the presentation of or interaction with the informationcontent data 222.

In one embodiment, once the one or more users are provided withinformation content data 222 and user experience data 224 through theuser interface 220, the interactions of the one or more users with theinformation content data 222 are monitored by user interaction datageneration module 226 through collection of user input data receivedthrough the user interface 220. The user input data collected by userinteraction data generation module 226 may include, but is not limitedto, data associated with click-stream input, textual input, touch input,gesture input, audio input, image input, video input, accelerometerinput, and/or physiological input. In one embodiment, once the userinput data is collected by user interaction data generation module 226,the user input data from each of the one or more users is processed andaggregated by user interaction data generation module 226 to generatecollective user interaction data 230.

In various embodiments, user interaction data may include data such as,but not limited to, the number of times a user accesses the applicationenvironment 218, the length of time a user spends engaging with theapplication environment 218, how long a user has had access to theapplication environment 218, the type of information content data 222that a user engages with the most while using the applicationenvironment 218, whether or not a user utilizes advanced inputmechanisms that may be provided by user interface 220, the type of inputmechanisms most preferred by a user, the speed at which a user interactswith the information content data 222 presented through the userinterface 220, and the level of comprehension a user has of theinformation content data 222 presented through the user interface 220.

In one embodiment, once collective user interaction data 230 has beengenerated by user interaction data generation module 226, the collectiveuser interaction data 230 is analyzed by collective user interactiondata analysis module 232 to generate average user interaction data 234.

In one embodiment, collective user interaction data analysis module 232analyzes collective user interaction data 230 to determine averagesacross one or more users or one or more groups of users with respect tothe individual types of user interaction data that form the collectiveuser interaction data 230. As noted above, examples of individual typesof user interaction data may include user interaction data such as userinteraction speed and user comprehension level. Further, each of the oneor more users may have multiple data points associated with each type ofuser interaction data. In addition, application environment 218 may beconfigured to group the collective user interaction data 230 based onuser characteristics such as, but not limited to, age, gender, and race.The collective user interaction data 230 may therefore be divided intoany number of groups and each of the groups may be consideredindividually, as a whole, or in any desired combination, in order togenerate average user interaction data 234.

In one embodiment, once the collective user interaction data 230 isanalyzed by collective user interaction data analysis module 232 andaverage user interaction data 234 is generated, the average userinteraction data 234 is utilized by threshold user interactiondefinition module 236 to define one or more threshold user interactiondifferentials, such that users whose user interaction data varies fromthe average user interaction data 234 can be identified. In oneembodiment, a threshold user interaction differential represents amaximum allowable variation between a specific user's interaction dataand the average user interaction data. In various embodiments, thethreshold user interaction differential may be defined in various ways,such as, but not limited to, through application configuration options,or use of a predetermined standard.

As already noted above, in various embodiments, a large number ofindividual possible averages may be generated during the generation ofthe average user interaction data 234, depending on the variousgroupings of users and user interaction data types, and as such, itfollows that there could potentially be a different threshold userinteraction differential associated with each of the averages that makeup average user interaction data 234. In one embodiment, this collectionof threshold user interaction differentials is aggregated by thresholduser interaction definition module 236 to generate threshold userinteraction differential data 238.

In one embodiment, once threshold user interaction differential data 238is generated by threshold user interaction definition module 236,current user computing system 208 of user computing environment 206,which is associated with a current user of application environment 218,is provided with information content data 222 and user experience data224 through the user interface 220.

In one embodiment, once the current user is provided with informationcontent data 222 and user experience data 224 through the user interface220, the interactions of the current user with the information contentdata 222 are monitored by user interaction data generation module 226through collection of user input data received through the userinterface 220. The user input data collected by user interaction datageneration module 226 may include, but is not limited to, dataassociated with click-stream input, textual input, touch input, gestureinput, audio input, image input, video input, accelerometer input,and/or physiological input. In one embodiment, once the current userinput data is collected by user interaction data generation module 226,the current user input data is processed and aggregated by userinteraction data generation module 226 to generate current userinteraction data 240.

In one embodiment, once current user interaction data 240 has beengenerated by user interaction data generation module 226, the currentuser interaction data 240 is analyzed along with the average userinteraction data 234, to generate current user interaction differentialdata 244, which represents any differential between the current userinteraction data 240 and the average user interaction data 234.

In one embodiment, the current user interaction data 240 is analyzed toextract the data that is most relevant to the type of user interactiondata the application environment 218 has been configured to monitor. Forexample, if the application environment 218 has been configured tomonitor user interaction speed and user comprehension level, then datarelated to the current user's interaction speed and the current user'scomprehension level is extracted from the current user interaction data240.

In one embodiment, once the relevant user interaction data has beenextracted from the current user interaction data 240, the average userinteraction data 234 is analyzed to determine the data in the averageuser interaction data 234 that corresponds to the relevant userinteraction data. The current user interaction data 240 is then comparedto the corresponding data in the average user interaction data 234 todetermine whether there is any differential between the current userinteraction data 240 and the corresponding data in the average userinteraction data 234. Current user interaction data analysis module 242then generates current user interaction differential data 244, whichrepresents any such differential between the current user interactiondata 240 and the corresponding data in the average user interaction data234.

In one embodiment, once the current user interaction data 240 isanalyzed along with the average user interaction data 234 to generatecurrent user interaction differential data 244, differential comparatormodule 246 compares the current user interaction differential data 244for one or more types of user interaction data with the threshold userinteraction differential data 238 corresponding to the same one or moretypes of user interaction data to determine whether one or more of thecurrent user interaction differentials in current user interactiondifferential data 244 is greater than the corresponding threshold userinteraction differentials in threshold user interaction differentialdata 238.

For example, in one embodiment, the current user interactiondifferential associated with user interaction speed may be compared tothe threshold interaction differential associated with user interactionspeed, and the current user interaction differential associated withuser comprehension level may be compared to the threshold interactiondifferential associated with user comprehension level. In this example,the comparison may yield that none, one, or both of the user interactiondifferentials is greater than their corresponding threshold interactiondifferentials.

In one embodiment, once the current user interaction differential data244 is compared with the threshold interaction differential data 238, ifone or more of the current user interaction differentials is found, bydifferential comparator module 246, to be greater than the correspondingthreshold interaction differentials, it may be determined that this isindicative of an anomaly in the psychological state of the user, and oneor more actions may be taken, as determined by action determinationmodule 248.

In one embodiment, the actions to be taken may be determined by actiondetermination module 248 based on the severity of the anomaly. Forexample, if the anomaly is minor, then action determination module 248may determine that actions should be taken to make slight adjustments tothe information content data 222 and/or the user experience data 224that is presented to the current user through the user interface 220. Onthe other hand, if the anomaly is severe, then action determinationmodule 248 may determine that actions should be taken to make majoradjustments to the information content data 222 and/or the userexperience data 224 that is presented to the current user through theuser interface 220. In other embodiments, action determination module248 may determine that more extreme actions should be taken. Forexample, if a current user is determined to be in a severely anxiousmental state, action determination module 248 may determine that actionssuch as emergency notifications and personal intervention areappropriate.

In various embodiments, once action determination module 248 determinesactions to be taken, control proceeds to action execution module 250 forexecution of the determined actions. Action execution may include, forexample, selecting and providing different information content data 222or user experience data 224 that is more appropriate for the currentuser's psychological state, contacting the user through any userapproved contact means, and/or contacting a user's trusted third partyon behalf of the user.

FIG. 3 is a flow chart of a process 300 for remotely identifying andmonitoring changes or anomalies in the psychological state ofapplication users based on historical user interaction data and currentuser interaction data in accordance with a second embodiment.

Process 300 begins at BEGIN 302 and process flow proceeds to 304. At304, a user of an application is provided with a user interface, whichallows the user to receive output from the application, as well as toprovide input to the application.

In various embodiments, the application may be any type of applicationthat is capable of providing content/information to a user through auser interface, including, but not limited to, a desktop computingsystem application, a mobile computing system application, a virtualreality computing system application, an application provided by anInternet of Things (IoT) device, or any combination thereof. In variousembodiments, the user interface may include any combination of agraphical user interface, an audio-based user interface, a touch-baseduser interface, or any other type of user interface currently known tothose of skill in the art, or any other type of user interface that maybe developed after the time of filing.

In one embodiment, the application provided to the user is a digitaltherapeutics application, which is designed to assist patients who havebeen diagnosed with one or more medical conditions. As a specificillustrative example, upon diagnosing a patient with one or more medicalconditions, a medical care professional may prescribe the patient accessto the digital therapeutics application. The digital therapeuticsapplication may be accessed by the patient through any type of computingsystem that is capable of providing a user interface to a user, asdiscussed above. Upon accessing the digital therapeutics application,the patient then becomes a user of the application, and is provided witha user interface, which enables the user to interact with the digitaltherapeutics application.

In one embodiment, once the user is provided with a user interface tothe application at 304, process flow proceeds to 306. In one embodiment,at 306, user profile data is obtained and/or generated and a userprofile is created for the user.

In some embodiments, the user profile may contain data such as, but notlimited to, the user's name, age, date of birth, gender, race, and/oroccupation. The user profile may further contain data related to theuser's individual sessions with the application, or data related to theuser's interactions with the application over time. In the example of adigital therapeutics application, in some embodiments, the user profilemay contain information specific to the application's field of use, suchas the user's medical history, medical conditions, medications, and/ormedical care providers.

In some embodiments, the user profile may be made accessible to theuser, and the user may be given permissions to view and modify one ormore parts of the profile. In other embodiments, the user profile is notmade accessible to the user, and is instead maintained solely for use bythe application and/or the application administrators. In otherembodiments, the user profile is not made accessible to the user, and isinstead accessible only by third parties, such as one or more medicalprofessionals. In some embodiments, some parts of the user profile maybe made accessible to the user or third parties, while other parts ofthe user profile may be inaccessible by the user or third parties.

In one embodiment, once a user profile is created for the user at 306,process flow proceeds to 308. At 308, the user is provided withinformation through the user interface.

In various embodiments, the information provided to the user through theuser interface includes, but is not limited to, textual information,audio information, graphical information, image information, videoinformation, and/or any combination thereof. In one embodiment, theinformation is provided to the user in such a way that allows the userto interact with the information provided. For example, the user may bepresented with information on the screen of an electronic device, alongwith a variety of graphical user elements, which allow the user toscroll through the information, click on buttons associated with theinformation, and/or enter textual strings in response to theinformation. When the information is presented to the user on a devicethat includes a touch screen, the interaction may include touch-basedinteractions and/or gesture recognition. In addition to textual inputsand touch or click-based inputs, in various embodiments, the user may beable to interact with the information through more advanced inputmechanisms such as through audio input, video input, accelerometerinput, voice recognition, facial recognition or through a variety ofphysiological sensors. Examples of physiological sensors may include,but are not limited to, heart rate monitors, blood pressure monitors,eye tracking monitors, or muscle activity monitors.

As one specific illustrative example, in one embodiment, once a user ofa digital therapeutics application is provided with a user interface,they may be provided with content-based information such as, but notlimited to, information related to medical history, current or potentialmedical care providers, medical conditions, medications, nutritionalsupplements, advice or suggestions regarding diet and/or exercise, orany other type of information that may be considered relevant to theuser.

In one embodiment, the content-based information may be provided solelyin a text format, however in various other embodiments, the user mayalso be presented with images that accompany the text, for example,images that depict one or more visual symptoms related to the user'smedical conditions. The user may further be presented with graphicalcontent, such charts, graphs, digital simulations, or othervisualization tools. As one illustrative example, the user might bepresented with a chart or graph that compares the user's symptoms withthose of other patients diagnosed with the same or similar conditions.The user may further be presented with audio and/or video informationrelated to their medical conditions. As additional illustrativeexamples, the user may be provided with one or more instructional videosthat guide the user through physical therapy exercises, or educationalvideos that inform the user about the history and/or science behindtheir medical conditions. In various embodiments, the user may bepresented with any combination of the above types of content-basedinformation, or any other additional types of content that may berelevant to the user.

In addition to the types of content-based information discussed above,another type of information that may be provided to the user isaesthetics-based information. This type of information may not beimmediately recognized by the user, but it nevertheless plays animportant role in the way in which the user absorbs and reacts to thepresentation of the content-based information. This aesthetics-basedinformation is used to create the overall user experience that isprovided to a user by an application, and thus may also be referred toherein as user experience information, or user experience data. Examplesof user experience data include, but are not limited to, the colors andfonts used to present the content-based information to a user, thevarious shapes of the graphical user interface elements, the layout orordering of the content-based information presented to a user, and/orthe sound effects, music, or other audio elements that may accompany thepresentation of or interaction with the content-based information.

In one embodiment, once the user is provided with information throughthe user interface at 308, process flow proceeds to 310. At 310, theinteractions of the user with the information presented through the userinterface are monitored over time and historical user interaction datais generated.

The interactions of the user with the information presented through theuser interface may be monitored through collection of user input datareceived through the user interface. The user input data collected mayinclude, but is not limited to, data associated with click-stream input,textual input, touch input, gesture input, audio input, image input,video input, accelerometer input, and/or physiological input.

In one embodiment, the user input data is collected and monitored overtime, on a per-session basis. For example, a user may access andinteract with the application several times per day, once per day, onceper week, etc., and each instance of access and interaction wouldconstitute an application session. In one embodiment, each time the userengages in an application session, the user input data is collected, andmay be stored as part of the user profile. Further, in one embodiment,each time the user input data is collected from the user for anapplication session, the user input data from each of the previoussessions is processed and aggregated to generate historical userinteraction data.

As one illustrative example, in one embodiment, a digital therapeuticsapplication may be configured to monitor specific types of userinteraction data, in order to enable further data analysis andprocessing. In one embodiment, the digital therapeutics application maybe configured to monitor the speed at which a user interacts with theinformation provided. In one embodiment, the speed at which the userinteracts with the information provided may be measured by collectingclickstream data, which may include data such as how long the userspends engaging with various parts of the information content presentedto the user.

For example, consider the situation where a user of a digitaltherapeutics application is presented with a lengthy article related toone or more of their medical conditions. In this example, the user wouldlikely need to fully scroll through the content to read the entirearticle. The time it takes for a user to scroll from the top of the textto the bottom of the text may be determined from the user input data,and this input data could then be used to generate user interaction datarepresenting the speed at which the user read, or interacted, with thearticle. The user interaction data representing speed of interaction forthis user, for this session, may then be stored as part of the userprofile and/or included as part of the user's historical userinteraction data.

As a further example, a user of a digital therapeutics application maybe presented with a series of screens, where each screen may contain oneor more types of information related to the user's medical conditions.For instance, the first screen may include text and images, the secondscreen may include one or more graphical visualizations, and the thirdscreen may include an audio/video presentation, along with textualinformation. Each screen may have user interface elements, such asnavigation buttons, allowing the user to move forward and backwardsbetween the different screens. The time it takes the user to click ortouch from one screen to the next, or from the beginning to the end ofthe presentation may be determined from the user input data, and thisinput data could then also be used to generate user interaction datarepresenting the speed at which the user read, or interacted with, thepresentation. Additionally, a user may be presented with a variety ofquestions or exercises requiring textual responses, and the frequency ofthe typing and deleting events could be used to generate userinteraction data representing the speed at which the user interactedwith the exercise materials.

Again, the user interaction data representing speed of interaction forthis user, for this session, may then be stored as part of the userprofile and/or included as part of the user's historical userinteraction data.

In another embodiment, the digital therapeutics application may beconfigured to monitor a user's interactions with the information todetermine the user's level of comprehension with respect to thatinformation. In one embodiment, the level of comprehension associatedwith the user and the information provided to the user may be measuredby periodically presenting the user with a variety of prompts orquestions designed to determine whether the user is engaged with andunderstanding the information being presented. A comprehension level maythen be calculated, for example, based on the percentage of questionsthat the user answered correctly.

Further, in one embodiment, a user's level of comprehension may bedetermined based on the percentage of the provided information that theuser read or interacted with. For example, if a user begins reading anarticle, but the user input data indicates that the user never scrollsto the end of the article, it may be determined that the user has poorcomprehension of the information provided. Likewise, in the case where auser is presented with multiple screens of information, for example, tenscreens, if the user only navigates to two of the ten screens, then itmay be determined that the user has poor comprehension of theinformation provided. The user interaction data representingcomprehension level for this user, for this session, may then be storedas part of the user profile and/or included as part of the user'shistorical user interaction data.

It should be noted here, that the foregoing examples are given forillustrative purposes only, and are not intended to limit the scope ofthe invention as disclosed herein and as claimed below.

In one embodiment, once the interactions of the user with theinformation presented through the user interface are monitored over timeand the associated historical user interaction data is generated at 310,process flow proceeds to 312. In one embodiment, at 312, the historicaluser interaction data is analyzed, and baseline user interaction data isgenerated.

As discussed above, in various embodiments, the historical userinteraction data may include, but is not limited to, data generatedbased on associated click-stream input, textual input, touch input,gesture input, audio input, image input, video input, accelerometerinput, and/or physiological input obtained through monitoring of theinteractions of the user with the information provided through the userinterface over time.

In one embodiment, at 312, the historical user interaction data isanalyzed to determine one or more user baselines, across one or more ofthe user's application sessions, with respect to individual types ofuser interaction data. For example, types of user interaction data mayinclude, but are not limited to, the number of times a user accesses theapplication, the length of time a user spends engaging with theapplication, how long a user has had access to the application, the typeof information that a user engages with the most while using theapplication, whether or not a user utilizes advanced input mechanisms,the type of input mechanisms most preferred by a user, the speed atwhich a user engages with the information presented through theapplication, and the level of comprehension a user has of theinformation presented through the application.

Consider the above described illustrative example, in which a digitaltherapeutics application is configured to monitor the speed at which auser engages with the information presented through the user interface,as well the level of comprehension a user has of the informationpresented through the user interface. In this specific illustrativeexample, at 312, the historical user interaction data would include dataindicating the speed at which the user interacted with the informationpresented during each of the user's application sessions, as well asdata indicating the level of comprehension that the user had withrespect to the information presented during each of the user'sapplication sessions. Thus, the user may have multiple associated datapoints that form part of the historical user interaction data. Forexample, the user may have a particular interaction speed and/orcomprehension level associated with a particular piece of information,received on a particular day. The same user may have a differentinteraction speed and/or comprehension level associated with the samepiece of information, received on a different day, etc. Further, it maybe considered desirable for the digital therapeutics application togroup the historical user data based, for example, on time segments. Assuch, the historical user data may be analyzed for various time periods,such as the past week, the past month, the past year, etc. Thus, thedigital therapeutics application may be configured to consider a varietyof factors when analyzing the historical user interaction data togenerate baseline user interaction data.

As one simplified illustrative example, the digital therapeuticsapplication may be configured to analyze the user's historical userinteraction data to calculate the user's baseline speed of interactionwith a particular set of informational content over the past month. Theapplication may further be configured to calculate the user's baselinelevel of comprehension of a different set of informational content overthe past year. The analysis may further be configured to ignore datapoints that fall outside of a predefined threshold when calculating theuser's baseline. Each of the calculated baselines would then beaggregated to generate the baseline user interaction data for thisparticular user.

In one embodiment, once the historical user interaction data is analyzedand baseline user interaction data is generated at 312, process flowproceeds to 314. In one embodiment, at 314, one or more thresholdchanges in user interaction data are defined and threshold userinteraction differential data is generated.

In one embodiment, one or more threshold changes in user interactiondata are defined, such that when the user's current user interactiondata varies from the user's baseline user interaction data, appropriateactions can be taken. In one embodiment, a threshold change in the userinteraction data represents a maximum allowable variation between theuser's current interaction data and the user's baseline interactiondata. In various embodiments, the threshold change in user interactiondata may be defined in various ways, such as, but not limited to,through application configuration options, or use of a predeterminedstandard.

For example, in one embodiment, after generation of baseline userinteraction data for a user, it may be determined that the user'sbaseline level of comprehension of a particular type of informationalcontent is 50%, where 50% represents the percentage of comprehensionquestions related to the content that were previously correctly answeredby the user. It may be decided by specialists, or other experts in thefield of use, that a 10% variance is relatively common, and as such, ifthe current user interaction data for this user indicated a 40%comprehension level with respect to this type of informational content,this would not raise concerns. However, if the threshold change in theuser interaction data for this particular type of content was defined at20% variance, then if the current user interaction data for this userindicated a 29% comprehension level with respect to this type ofinformational content, this would raise concerns, and further actionmight be deemed appropriate, as will be discussed in further detailbelow.

As already noted above, in various embodiments, multiple user baselinesmay be generated during the generation of the baseline user interactiondata at 312, and as such, it follows from the preceding discussion thatthere could potentially be a different threshold change in userinteraction data associated with each of the individual baselines thatform the baseline user interaction data. In one embodiment, thiscollection of threshold changes in user interaction data is aggregatedto generate threshold user interaction differential data.

In one embodiment, once one or more threshold changes in userinteraction data are defined and threshold user interaction differentialdata is generated at 314, process flow proceeds to 316. In oneembodiment, at 316, the user of the application is provided with currentinformation through the user interface of the application.

In contrast to operation 308 described above, where the user is providedwith information through the application user interface over time, at316, the user is provided with information through the user interface ofthe application during a single current session of using theapplication. Therefore, the information provided to the user during thissingle current session may hereafter be referred to as currentinformation.

As described in detail above, with respect to information provided tothe user through the user interface, in various embodiments, the currentinformation provided to the user through the user interface includes,but is not limited to, textual information, audio information, graphicalinformation, image information, video information, user experienceinformation, and/or any combination thereof. In one embodiment, thecurrent information is provided to the user in such a way that allowsthe user to interact with the information provided.

In one embodiment, once current information is provided to the user at316, process flow proceeds to 318. In contrast to operation 310described above, where the user's interactions with the informationprovided through the user interface are monitored over time to generatehistorical user interaction data, at 318, the user's interactions withthe current information provided through the user interface aremonitored to generate current user interaction data.

As described in detail above, with respect to monitoring theinteractions of the user over time to generate historical userinteraction data, in various embodiments, the interactions of the userwith the current information presented through the user interface may bemonitored through collection of user input data received through theuser interface. The user input data collected may include, but is notlimited to, data associated with click-stream input, textual input,touch input, gesture input, audio input, image input, video input,accelerometer input, and/or physiological input. In one embodiment, oncethe user input data is collected from the user, the user input data isprocessed and aggregated to generate current user interaction data.

As also described in detail above, with respect to monitoring theinteractions of the user over time to generate historical userinteraction data, in various embodiments, the application may beconfigured to monitor specific types of user interaction data, such as,but not limited to, the speed at which the user interacts with thecurrent information provided, and/or the user's level of comprehensionwith respect to the current information provided. In one embodiment, thespeed at which the user interacts with the current information providedmay be measured by collecting clickstream data, which may include datasuch as how long the user spends engaging with various parts of thecurrent information content presented to the user through the userinterface. In one embodiment, the level of comprehension associated withthe user and the current information provided may be measured byperiodically presenting the user with a variety of prompts or questionsdesigned to determine whether the user is engaged with and understandingthe current information being presented. A comprehension level may thenbe calculated, for example, based on the percentage of questions thatthe user answered correctly. Further, in one embodiment, the user'slevel of comprehension may be determined based on the percentage of thecurrently provided information that the user read or interacted with.

In one embodiment, once the user's interactions with the currentinformation provided through the user interface are monitored togenerate current user interaction data at 318, process flow proceeds to320. In one embodiment, at 320, the current user interaction data isanalyzed along with the baseline user interaction data, to generatecurrent user interaction differential data, which represents anydifferential between the current user interaction data and the baselineuser interaction data.

In one embodiment, the current user interaction data is analyzed toextract the data that is most relevant to the type of user interactiondata the application has been configured to monitor. For example, if theapplication has been configured to monitor user interaction speed anduser comprehension level, then data related to the user's speed ofinteraction with the current information and the user's level ofcomprehension of the current information is extracted from the currentuser interaction data.

In one embodiment, once the relevant user interaction data has beenextracted from the current user interaction data, the baseline userinteraction data is analyzed to determine the data in the baseline userinteraction data that corresponds to the relevant user interaction data.The current user interaction data is then compared to the correspondingdata in the baseline user interaction data to determine whether there isany differential between the current user interaction data and thecorresponding data in the baseline user interaction data, and currentuser interaction differential data is generated, which represents anysuch differential between the current user interaction data and thecorresponding data in the baseline user interaction data.

Returning to the illustrative example of the digital therapeuticsapplication described above, if the relevant user interaction data isdata associated with speed of interaction, then the user's baseline userinteraction data would be analyzed to extract the data that provides theuser's baseline interaction speed. If, for example, the user'sinteraction speed with respect to the current information is measured tobe 150 words per minute, and the user's baseline interaction speed is200 words per minute, then the differential between the user'sinteraction speed with respect to the current information and the user'sbaseline interaction speed would be 50 words per minute, and this valuewould be represented by the current user interaction differential data.In various embodiments, the current user interaction differential dataincludes differential data related to multiple types of user interactiondata. For example, the current user interaction differential data mayinclude, but is not limited to, differential data related to user'sspeed of interaction, as well as differential data related to the user'scomprehension level.

As already noted several times above, the foregoing examples are givenfor illustrative purposes only, and are not intended to limit the scopeof the invention as disclosed herein and as claimed below. As oneexample, user interaction speed may be measured using any means ofmeasurement available, and should not be construed herein as limited toa measurement requiring words per minute.

In one embodiment, once the current user interaction data is analyzedalong with the baseline user interaction data to generate current userinteraction differential data at 320, process flow proceeds to 322. At322, the current user interaction differential data for one or moretypes of user interaction data is compared with the threshold userinteraction differential data corresponding to the same one or moretypes of user interaction data to determine whether one or more of thecurrent user interaction differentials is greater than the correspondingthreshold user interaction differentials.

For example, in one embodiment, the current user interactiondifferential associated with user interaction speed may be compared tothe threshold user interaction differential associated with userinteraction speed, and the current user interaction differentialassociated with user comprehension level may be compared to thethreshold user interaction differential associated with usercomprehension level. In this example, the comparison may yield thatnone, one, or both of the user interaction differentials is greater thantheir corresponding threshold user interaction differentials.

In one embodiment, once the current user interaction differential datais compared with the threshold user interaction differential data at322, process flow proceeds to 324. At 324, if one or more of the currentuser interaction differentials is greater than the correspondingthreshold user interaction differentials, it may be determined that thisis indicative of a change or anomaly in the psychological state of theuser, and this data may be utilized to arrive at one or more predictionsregarding the current user's mental state. Upon identifying the currentuser's mental state and/or identifying changes or anomalies in thecurrent user's mental state, one or more actions may be taken.

In one embodiment, the actions to be taken may be determined based onthe severity of the anomaly. For example, if the anomaly is minor, thenactions might be taken to make slight adjustments to the informationcontent data and/or the user experience data that is presented to theuser. On the other hand, if the anomaly is severe, then actions might betaken to make extreme adjustments to the information content data and/orthe user experience data that is presented to the user. In oneembodiment, adjustments to the information content data may includeadjustments such as, but not limited to, providing textual content thatuses gentler language, providing audio content that includes quieter,more relaxing voices, sounds, or music, or providing image/video contentthat is less realistic or less graphic. Adjustments to the userexperience data may include adjustments such as, but not limited to,changing the colors, fonts, shapes, presentation, and/or layout of theinformation content data presented to the user.

For example, in one embodiment, as discussed above, the application is adigital therapeutics application, and the user is a patient who has beendiagnosed with a medical condition. Many patients experience a greatdeal of anxiety related to their medical conditions. If an anomaly isdetected in the psychological state of the user, this may indicate thatthe user is experiencing a higher than normal level of anxiety, andtherefore may benefit from assistance, or from adjustments designed toreduce the user's anxiety level.

As one specific illustrative example, if a determination is made thatthe user is slightly more anxious than they usually are, minor actionsmay be taken to reduce the user's anxiety level, such as adjusting thecontent and/or presentation of the information that is being provided tothe user through the user interface. As one simplified illustrativeexample, cool colors such as blue and violet are known to producecalming effects, and rounder, softer shapes are also associated withcalming effects. So in this situation, the user experience content datamay be modified so that the content is presented to the user with ablue/violet color scheme, and the graphical user elements may be changedto include rounder and softer shapes. As another specific illustrativeexample, if a determination is made that the user is significantly moreanxious than they usually are, more extreme actions may be taken, suchas notifying one or more medical professionals associated with the userthrough a notification system of the application, or some other form ofpersonal intervention from one or more medical professionals associatedwith the user.

In various embodiments several additional types of actions may beappropriate specifically when dealing with users who have been diagnosedwith a medical condition, such as, but not limited to: asking the userfor input and/or response data; alerting the user; alerting one or moreof the user's mental health or medical professionals; making notes in,adding data to, or highlighting the user's electronic file; making aspecialist referral; recommending support contacts to the user;prescribing additional appointments, treatments, actions, ormedications; calling emergency response or intervention professionals;notifying emergency contacts, relatives, or caregivers, etc.

In one embodiment, once one or more actions are taken based on the userinteraction data at 324, process flow proceeds to END 326 and theprocess 300 for remotely identifying and monitoring changes or anomaliesin the psychological state of application users based on historical userinteraction data and current user interaction data is exited to awaitnew data and/or instructions.

FIG. 4 is a block diagram of a production environment 400 for remotelyidentifying and monitoring changes or anomalies in the psychologicalstate of application users based on historical user interaction data andcurrent user interaction data in accordance with a second embodiment.

In one embodiment, production environment 400 includes user computingenvironment 402, and service provider computing environment 410. Usercomputing environment 402 further comprises user computing system 404.The computing environments 402 and 410 are communicatively coupled toeach other with one or more communication networks 416.

In one embodiment, service provider computing environment 410 includesprocessor 412, physical memory 414, and application environment 418.Processor 412 and physical memory 414 coordinate the operation andinteraction of the data and data processing modules associated withapplication environment 418. In one embodiment, application environment418 includes user interface 420, which is provided to user computingsystem 404 through the one or more communication networks 416.

In one embodiment, application environment 418 further includes userinteraction data generation module 426, historical user interaction dataanalysis module 432, threshold user interaction definition module 436,current user interaction data analysis module 442, differentialcomparator module 446, action determination module 448, and actionexecution module 450, each of which will be discussed in further detailbelow.

Additionally, in one embodiment, application environment 418 includesinformation content data 422, user experience data 424, user profiledata 429, historical user interaction data 430, baseline userinteraction data 434, threshold user interaction differential data 438,current user interaction data 440, and current user interactiondifferential data 444, each of which will be discussed in further detailbelow. In some embodiments, user profile data 429, historical userinteraction data 430, baseline user interaction data 434, and currentuser interaction data 440 may be stored in user database 428, whichincludes data associated with one or more users of applicationenvironment 418.

In one embodiment, user computing system 404 of user computingenvironment 402, which is associated with a single user of applicationenvironment 418, is provided with a user interface 420, which allows theuser to receive output from the application environment 418, as well asto provide input to the application environment 418, through the one ormore communication networks 416.

As discussed above, in various embodiments, the application environment418 may be any type of application environment that is capable ofproviding a user interface and content/information to a user, including,but not limited to, a desktop computing system application, a mobilecomputing system application, a virtual reality computing systemapplication, an application provided by an Internet of Things (IoT)device, or any combination thereof. Further, in various embodiments, theuser interface 420 may include any combination of a graphical userinterface, an audio-based user interface, a touch-based user interface,or any other type of user interface currently known to those of skill inthe art, or any other type of user interface that may be developed afterthe time of filing.

In one embodiment, once the user is provided with user interface 420,user profile data 429 is obtained and/or generated and a user profile iscreated for the user. In some embodiments, the user profile may containdata such as, but not limited to, the user's name, age, date of birth,gender, race, and/or occupation. The user profile may further containdata related to the user's individual sessions with the applicationenvironment 418, or data related to the user's interactions with theapplication environment 418 over time.

In one embodiment, once user profile data 429 is used to create aprofile for the user, user computing system 404 of user computingenvironments 402, which is associated with a single user of applicationenvironment 418, is provided with information content data 422 and userexperience data 424 through the user interface 420.

In various embodiments, the information content data 422 provided to theuser through the user interface 420 includes, but is not limited to,textual information, audio information, graphical information, imageinformation, video information, and/or any combination thereof. In oneembodiment, the information content data 422 is provided to the user insuch a way that allows the user to interact with the information contentdata 422.

In various embodiments, the user experience data 424 includes, but isnot limited to, colors and fonts used to present the information contentdata 422 to the user, the various shapes of graphical user interfaceelements, the layout or ordering of the information content data 422,and/or the sound effects, music, or other audio elements that mayaccompany the presentation of or interaction with the informationcontent data 422.

In one embodiment, once the user is provided with information contentdata 422 and user experience data 424 through the user interface 420,the interactions of the user with the information content data 422 aremonitored over time by user interaction data generation module 426through collection of user input data received through the userinterface 420. The user input data collected by user interaction datageneration module 426 may include, but is not limited to, dataassociated with click-stream input, textual input, touch input, gestureinput, audio input, image input, video input, accelerometer input,and/or physiological input. In one embodiment, once the user input datais collected by user interaction data generation module 426, the userinput data from each of the user's previous application sessions isprocessed and aggregated by user interaction data generation module 426to generate historical user interaction data 430.

In various embodiments, user interaction data may include data such as,but not limited to, the number of times a user accesses the applicationenvironment 418, the length of time a user spends engaging with theapplication environment 418, how long a user has had access to theapplication environment 418, the type of information content data 422that a user engages with the most while using the applicationenvironment 418, whether or not a user utilizes advanced inputmechanisms that may be provided by user interface 420, the type of inputmechanisms most preferred by a user, the speed at which a user interactswith the information content data 422 presented through the userinterface 420, and the level of comprehension a user has of theinformation content data 422 presented through the user interface 420.

In one embodiment, once historical user interaction data 430 has beengenerated by user interaction data generation module 426, the historicaluser interaction data 430 is analyzed by historical user interactiondata analysis module 432 to generate baseline user interaction data 434.

In one embodiment, historical user interaction data analysis module 432analyzes historical user interaction data 430 to determine one or moreuser baselines, across one or more of the user's application sessions,with respect to the individual types of user interaction data that formthe historical user interaction data 430. As noted above, examples ofindividual types of user interaction data may include user interactiondata such as user interaction speed and user comprehension level.Further, the user may have multiple data points associated with eachtype of user interaction data. In addition, application environment 418may be configured to group the historical user interaction data 430based on factors, such as, but not limited to, time periods associatedwith the user interaction data. The historical user interaction data 430may therefore be divided into any number of segments and each of thesegments may be considered individually, as a whole, or in any desiredcombination, in order to generate baseline user interaction data 434.

In one embodiment, once the historical user interaction data 430 isanalyzed by historical user interaction data analysis module 432 andbaseline user interaction data 434 is generated, the baseline userinteraction data 434 is utilized by threshold user interactiondefinition module 436 to define one or more threshold changes in userinteraction data, such that when the user's current user interactiondata 440 varies from the user's baseline user interaction data 434,appropriate actions can be taken. In one embodiment, a threshold changein user interaction data represents a maximum allowable variationbetween a user's current user interaction data 440 and the user'sbaseline user interaction data 434. In various embodiments, thethreshold change in user interaction data may be defined in variousways, such as, but not limited to, through application configurationoptions, or use of a predetermined standard.

As already noted above, in various embodiments, multiple user baselinesmay be generated during the generation of the baseline user interactiondata 434, and as such, it follows that there could potentially be adifferent threshold change in user interaction data associated with eachof the individual baselines that form the baseline user interaction data434. In one embodiment, this collection of threshold changes in userinteraction data is aggregated by threshold user interaction definitionmodule 436 to generate threshold user interaction differential data 438.

In one embodiment, once threshold user interaction differential data 438is generated by threshold user interaction definition module 436, usercomputing system 404 of user computing environment 402, which isassociated with a single user of application environment 418, isprovided with current information content data 422 and current userexperience data 424 through the user interface 420.

In one embodiment, once the user is provided with current informationcontent data 422 and current user experience data 424 through the userinterface 420, the interactions of the user with the current informationcontent data 422 are monitored by user interaction data generationmodule 426 through collection of user input data received through theuser interface 420. The user input data collected by user interactiondata generation module 426 may include, but is not limited to, dataassociated with click-stream input, textual input, touch input, gestureinput, audio input, image input, video input, accelerometer input,and/or physiological input. In one embodiment, once the current userinput data is collected by user interaction data generation module 426,the current user input data is processed and aggregated by userinteraction data generation module 426 to generate current userinteraction data 440.

In one embodiment, once current user interaction data 440 has beengenerated by user interaction data generation module 426, the currentuser interaction data 440 is analyzed along with the baseline userinteraction data 434, to generate current user interaction differentialdata 444, which represents any differential between the current userinteraction data 440 and the baseline user interaction data 434.

In one embodiment, the current user interaction data 440 is analyzed toextract the data that is most relevant to the type of user interactiondata the application environment 418 has been configured to monitor. Forexample, if the application environment 418 has been configured tomonitor user interaction speed and user comprehension level, then datarelated to the user's speed of interaction with the current informationcontent data 422 and the user's level of comprehension of the currentinformation content data 422 is extracted from the current userinteraction data 440.

In one embodiment, once the relevant user interaction data has beenextracted from the current user interaction data 440, the baseline userinteraction data 434 is analyzed to determine the data in the baselineuser interaction data 434 that corresponds to the relevant userinteraction data. The current user interaction data 440 is then comparedto the corresponding data in the baseline user interaction data 434 todetermine whether there is any differential between the current userinteraction data 440 and the corresponding data in the baseline userinteraction data 434. Current user interaction data analysis module 442then generates current user interaction differential data 444, whichrepresents any such differential between the current user interactiondata 440 and the corresponding data in the baseline user interactiondata 434.

In one embodiment, once the current user interaction data 440 isanalyzed along with the baseline user interaction data 434 to generatecurrent user interaction differential data 444, differential comparatormodule 446 compares the current user interaction differential data 444for one or more types of user interaction data with the threshold userinteraction differential data 438 corresponding to the same one or moretypes of user interaction data to determine whether one or more of thecurrent user interaction differentials in current user interactiondifferential data 444 is greater than the corresponding threshold userinteraction differentials in threshold user interaction differentialdata 438.

For example, in one embodiment, the current user interactiondifferential associated with user interaction speed may be compared tothe threshold interaction differential associated with user interactionspeed, and the current user interaction differential associated withuser comprehension level may be compared to the threshold interactiondifferential associated with user comprehension level. In this example,the comparison may yield that none, one, or both of the user interactiondifferentials is greater than their corresponding threshold interactiondifferentials.

In one embodiment, once the current user interaction differential data444 is compared with the threshold user interaction differential data438, if one or more of the current user interaction differentials isfound, by differential comparator module 446, to be greater than thecorresponding threshold interaction differentials, this may beidentified as an anomaly in the psychological state of the user, and oneor more actions may be taken, as determined by action determinationmodule 448.

In one embodiment, the actions to be taken may be determined by actiondetermination module 448 based on the severity of the anomaly. Forexample, if the anomaly is minor, then action determination module 448may determine that actions should be taken to make slight adjustments tothe information content data 422 and/or the user experience data 424that is presented to the user through the user interface 420. On theother hand, if the anomaly is severe, then action determination module448 may determine that actions should be taken to make major adjustmentsto the information content data 422 and/or the user experience data 424that is presented to the current user through the user interface 420. Inother embodiments, action determination module 448 may determine thatmore extreme actions should be taken. For example, if a user isdetermined to be in a severely anxious mental state, actiondetermination module 448 may determine that actions such as emergencynotifications and personal intervention are appropriate.

In various embodiments, once action determination module 448 determinesactions to be taken, control proceeds to action execution module 450 forexecution of the determined actions. Action execution may include, forexample, selecting and providing different information content data 422or user experience data 424 that is more appropriate for the currentuser's psychological state, contacting the user through any userapproved contact means, and/or contacting a user's trusted third partyon behalf of the user.

FIG. 5 is a flow chart of a process 500 for remotely identifying orpredicting the psychological state of application users based on machinelearning-based analysis and processing in accordance with a thirdembodiment.

Process 500 begins at BEGIN 502 and process flow proceeds to 504. At504, one or more users of an application are provided with a userinterface, which allows the one or more users to receive output from theapplication, as well as to provide input to the application.

In various embodiments, the application may be any type of applicationthat is capable of providing content/information to a user through auser interface, including, but not limited to, a desktop computingsystem application, a mobile computing system application, a virtualreality computing system application, an application provided by anInternet of Things (IoT) device, or any combination thereof. In variousembodiments, the user interface may include any combination of agraphical user interface, an audio-based user interface, a touch-baseduser interface, or any other type of user interface currently known tothose of skill in the art, or any other type of user interface that maybe developed after the time of filing.

In one embodiment, the application provided to the one or more users isa digital therapeutics application, which is designed to assist patientswho have been diagnosed with one or more medical conditions. As aspecific illustrative example, upon diagnosing a patient with one ormore medical conditions, a medical care professional may prescribe thepatient access to the digital therapeutics application. The digitaltherapeutics application may be accessed by the patient through any typeof computing system that is capable of providing a user interface to auser, as discussed above. Upon accessing the digital therapeuticsapplication, the patient then becomes a user of the application, and isprovided with a user interface, which enables the user to interact withthe digital therapeutics application.

In one embodiment, once one or more users of an application are providedwith a user interface at 504, process flow proceeds to 506. At 506, theone or more users are provided with information through the userinterface.

In various embodiments, the information provided to the one or moreusers through the user interface includes, but is not limited to,textual information, audio information, graphical information, imageinformation, video information, and/or any combination thereof. In oneembodiment, the information is provided to the one or more users in sucha way that allows the one or more users to interact with the informationprovided. For example, a user may be presented with information on thescreen of an electronic device, along with a variety of graphical userelements, which allow the user to scroll through the information, clickon buttons associated with the information, and/or enter textual stringsin response to the information. When the information is presented to auser on a device that includes a touch screen, the interaction mayinclude touch-based interactions and/or gesture recognition. In additionto textual inputs and touch or click-based inputs, in variousembodiments, the user may be able to interact with the informationthrough more advanced input mechanisms such as through audio input,video input, accelerometer input, voice recognition, facial recognitionor through a variety of physiological sensors. Examples of physiologicalsensors may include, but are not limited to, heart rate monitors, bloodpressure monitors, eye tracking monitors, or muscle activity monitors.

As one specific illustrative example, in one embodiment, once one ormore users of a digital therapeutics application are provided with auser interface, they may be provided with content-based information suchas, but not limited to, information related to medical history, currentor potential medical care providers, medical conditions, medications,nutritional supplements, advice or suggestions regarding diet and/orexercise, or any other type of information that may be consideredrelevant to the one or more users.

In one embodiment, the content-based information may be provided solelyin a text format, however in various other embodiments, a user may alsobe presented with images that accompany the text, for example, imagesthat depict one or more visual symptoms related to the user's medicalconditions. The user may further be presented with graphical content,such charts, graphs, digital simulations, or other visualization tools.As one illustrative example, a user might be presented with a chart orgraph that compares the user's symptoms with those of other patientsdiagnosed with the same or similar conditions. The user may further bepresented with audio and/or video information related to their medicalconditions. As additional illustrative examples, the user may beprovided with one or more instructional videos that guide the userthrough physical therapy exercises, or educational videos that informthe user about the history and/or science behind their medicalconditions. In various embodiments, the user may be presented with anycombination of the above types of content-based information, or anyother additional types of content that may be relevant to the particularuser.

In addition to the types of content-based information discussed above,another type of information that may be provided to the one or moreusers is aesthetics-based information. This type of information may notbe immediately recognized by a user, but it nevertheless plays animportant role in the way in which the user absorbs and reacts to thepresentation of the content-based information. This aesthetics-basedinformation is used to create the overall user experience that isprovided to a user by an application, and thus may also be referred toherein as user experience information, or user experience data. Examplesof user experience data include, but are not limited to, the colors andfonts used to present the content-based information to a user, thevarious shapes of the graphical user interface elements, the layout orordering of the content-based information presented to a user, and/orthe sound effects, music, or other audio elements that may accompany thepresentation of or interaction with the content-based information.

In one embodiment, once the one or more users are provided withinformation through the user interface at 506, process flow proceeds to508. At 508, the interactions of the one or more users with theinformation presented through the user interface are monitored and userinteraction data is generated.

The interactions of one or more users with the information presentedthrough the user interface may be monitored through collection of userinput data received through the user interface. The user input datacollected may include, but is not limited to, data associated withclick-stream input, textual input, touch input, gesture input, audioinput, image input, video input, accelerometer input, and/orphysiological input. In one embodiment, once the user input data iscollected from the one or more users, the user input data from each ofthe one or more users is processed and aggregated to generate userinteraction data.

As one illustrative example, in one embodiment, a digital therapeuticsapplication may be configured to monitor specific types of userinteraction data, in order to enable further data analysis andprocessing. In one embodiment, the digital therapeutics application maybe configured to monitor the speed at which one or more users interactwith the information provided. In one embodiment, the speed at which auser interacts with the information provided may be measured bycollecting clickstream data, which may include data such as how long auser spends engaging with various parts of the information contentpresented to the user.

For example, consider the situation where a user of a digitaltherapeutics application is presented with a lengthy article related toone or more of their medical conditions. In this example, the user wouldlikely need to fully scroll through the content to read the entirearticle. The time it takes for a user to scroll from the top of the textto the bottom of the text may be determined from the user input data,and this input data could then be used to generate user interaction datarepresenting the speed at which the user read, or interacted, with thearticle.

As a further example, a user of a digital therapeutics application maybe presented with a series of screens, where each screen may contain oneor more types of information related to the user's medical conditions.For instance, the first screen may include text and images, the secondscreen may include one or more graphical visualizations, and the thirdscreen may include an audio/video presentation, along with textualinformation. Each screen may have user interface elements, such asnavigation buttons, allowing the user to move forward and backwardsbetween the different screens. The time it takes the user to click ortouch from one screen to the next, or from the beginning to the end ofthe presentation may be determined from the user input data, and thisinput data could then also be used to generate user interaction datarepresenting the speed at which the user read, or interacted with, thepresentation.

Additionally, a user may be presented with a variety of questions orexercises requiring textual responses, and the frequency of the typingand deleting events could be used to generate user interaction datarepresenting the speed at which the user interacted with the exercisematerials.

In another embodiment, the digital therapeutics application may beconfigured to monitor one or more users' interactions with theinformation to determine the one or more users' level of comprehensionwith respect to that information. In one embodiment, the level ofcomprehension associated with a user and the information provided to theuser may be measured by periodically presenting the user with a varietyof prompts or questions designed to determine whether the user isengaged with and understanding the information being presented. Acomprehension level may then be calculated, for example, based on thepercentage of questions that the user answered correctly.

Further, in one embodiment, a user's level of comprehension may bedetermined based on the percentage of the provided information that theuser read or interacted with. For example, if a user begins reading anarticle, but the user input data indicates that the user never scrollsto the end of the article, it may be determined that the user has poorcomprehension of the information provided. Likewise, in the case where auser is presented with multiple screens of information, for example, tenscreens, if the user only navigates to two of the ten screens, then itmay be determined that the user has poor comprehension of theinformation provided.

It should be noted here, that the foregoing examples are given forillustrative purposes only, and are not intended to limit the scope ofthe invention as disclosed herein and as claimed below.

In one embodiment, once the interactions of the one or more users withthe information presented through the user interface are monitored andthe associated user interaction data is generated at 508, process flowproceeds to 510. In one embodiment, at 510, user mental state data isobtained for each of the one or more users, and the user interactiondata for each of the one or more users is correlated with the mentalstate data corresponding to each of the one or more users.

In one embodiment, at 510 the user mental state data is obtained fromthe one or more users by interviewing each of the one or more usersbefore, after, or during generation of the user interaction data at 508.In some embodiments, at 510, the user mental state data is obtained byconsulting with a third party, such as a medical professional associatedwith the user, before or after the user interaction data is generated at508. In some embodiments, at 510 the user mental state data is obtainedfrom data in one or more files associated with a user indicating none ormore events occurring before or after the user interaction data isgenerated at 508. Such events may include, but are not limited to, achange in diagnosis of the user's health, a change in medication, or anyother event indicating the mental state of the user at or near the timethe user interaction data was generated at 508.

Once user mental state data is obtained for one or more users,indicating the mental state of each user at or near the time the userinteraction data was generated, the user mental state data for each useris correlated with the user interaction data that was generated for thatuser at 508. The correlated user mental state data and user interactiondata for each of the one or more users is then aggregated to generatecorrelated user interaction and mental state data.

In one embodiment, once the correlated user interaction and mental statedata is generated at 510, process flow proceeds to 512. In oneembodiment, at 512, the correlated user interaction and mental statedata is used as training data to create one or more trained machinelearning based mental state prediction models.

In various embodiments, and largely depending on the machine learningbased models used, the user interaction and/or mental state data isprocessed using various methods know in the machine learning arts toidentify elements and vectorize the user interaction and/or mental statedata. As a specific illustrative example, in a case where the machineleaning based model is a supervised model, the user interaction data canbe analyzed and processed to identify individual elements found to beindicative of a user's mental state. These individual elements are thenused to create user interaction data vectors in multidimensional spacewhich are, in turn, used as input data for training one or more machinelearning models. The mental state data for a user that correlates withthe user interaction data vector associated with that user is then usedas a label for the resulting vector. In various embodiments, thisprocess is repeated for the user interaction and mental state datareceived from each of the one or more users, such that multiple, oftenmillions, of correlated pairs of user interaction data vectors andmental state data are used to train one or more machine learning basedmodels. Consequently, this process results in the creation of one ormore trained machine learning based mental state prediction models.

Those of skill in the art will readily recognize that there are manydifferent types of machine learning based models known in the art, andas such, it should be noted that the specific illustrative example of asupervised machine learning based model discussed above should not beconstrued as limiting the embodiments set forth herein.

For instance, in various embodiments, the one or more machine learningbased models can be one or more of: supervised machine learning-basedmodels; semi supervised machine learning-based models; unsupervisedmachine learning-based models; classification machine learning-basedmodels; logistical regression machine learning-based models; neuralnetwork machine learning-based models; deep learning machinelearning-based models; and/or any other machine learning based modelsdiscussed herein, known at the time of filing, or as developed/madeavailable after the time of filing.

In one embodiment, once the correlated user interaction and mental statedata is used as training data to create one or more trained machinelearning based mental state prediction models at 512, process flowproceeds to 514. In one embodiment, at 514, a current user of theapplication is provided with information through the user interface ofthe application.

In contrast to operation 506 described above, where one or more usersare provided with information through the application user interface, at514, a single specific user is provided with information through theuser interface of the application, during a single current session ofusing the application. Therefore, the single specific user may hereafterbe referred to as the current user.

As described in detail above, with respect to information provided toone or more users, in various embodiments, the information provided tothe current user through the user interface includes, but is not limitedto, textual information, audio information, graphical information, imageinformation, video information, user experience information, and/or anycombination thereof. In one embodiment, the information is provided tothe current user in such a way that allows the current user to interactwith the information provided.

In one embodiment, once information is provided to a current user at514, process flow proceeds to 516. In contrast to operation 508described above, where one or more users' interactions with theinformation provided through the user interface are monitored togenerate user interaction data, at 516, the current user's interactionswith the information provided through the user interface are monitoredto generate current user interaction data.

As described in detail above, with respect to monitoring theinteractions of one or more users to generate user interaction data, invarious embodiments, the interactions of the current user with theinformation presented through the user interface may be monitoredthrough collection of user input data received through the userinterface. The user input data collected may include, but is not limitedto, data associated with click-stream input, textual input, touch input,gesture input, audio input, image input, video input, accelerometerinput, and/or physiological input. In one embodiment, once the userinput data is collected from the current user, the user input data isprocessed and aggregated to generate current user interaction data.

As also described in detail above, with respect to monitoring theinteractions of one or more users to generate collective userinteraction data, in various embodiments, the application may beconfigured to monitor specific types of current user interaction data,such as, but not limited to, the speed at which the current userinteracts with the information provided, and/or the current user's levelof comprehension with respect to the information provided. In oneembodiment, the speed at which the current user interacts with theinformation provided may be measured by collecting clickstream data,which may include data such as how long the current user spends engagingwith various parts of the information content presented to the currentuser through the user interface. In one embodiment, the level ofcomprehension associated with the current user and the informationprovided may be measured by periodically presenting the current userwith a variety of prompts or questions designed to determine whether thecurrent user is engaged with and understanding the information beingpresented. A comprehension level may then be calculated, for example,based on the percentage of questions that the current user answeredcorrectly. Further, in one embodiment, the current user's level ofcomprehension may be determined based on the percentage of the providedinformation that the current user read or interacted with.

In one embodiment, once the current user's interactions with theinformation provided through the user interface are monitored togenerate current user interaction data at 516, process flow proceeds to518. In one embodiment, at 518, the current user interaction data isprovided to the one or more trained machine learning based mental stateprediction models to generate current user mental state prediction data.

In one embodiment, the current user interaction data generated at 516 isvectorized to generate one or more user interaction data vectors. Theone or more user interaction data vectors associated with the currentuser are then provided as input data to the one or more trained machinelearning based mental state prediction models. The current userinteraction vector data is then processed to find a distance between theone or more current user interaction data vectors and one or morepreviously labeled user interaction data vectors, where the previouslylabeled user interaction data vectors are vectors with known associateduser mental state data. In one embodiment, one or more probabilityscores are determined based on a calculated distance between the currentuser interaction vector data and the previously labeled user interactionvector data. Upon determination that the one or more current userinteraction data vectors correlate to a user mental state associatedwith the previously labeled user interaction vector data, current usermental state prediction data is generated. In one embodiment, thecurrent user mental state prediction data comprises one or moreprobability scores, which indicate the probability that the current useris in one or more particular mental states.

In one embodiment, once current user mental state prediction data isgenerated at 518, process flow proceeds to 520. At 520, one or moreactions are taken based, at least in part, on current user mental stateprediction data received from the one or more trained machine learningbased mental state prediction models.

In one embodiment, the one or more actions to be taken may be determinedbased on the current user mental state prediction data. For example, ifthe current user mental state prediction data indicates that the currentuser is mildly anxious, then actions might be taken to make slightadjustments to the information content data and/or the user experiencedata that is presented to the current user. On the other hand, if thecurrent user mental state prediction data indicates that the currentuser is severely anxious, then actions might be taken to make majoradjustments to the information content data and/or the user experiencedata that is presented to the current user. In one embodiment,adjustments to the information content data may include adjustments suchas, but not limited to, providing textual content that uses gentlerlanguage, providing audio content that includes quieter, more relaxingvoices, sounds, or music, or providing image/video content that is lessrealistic or less graphic. Adjustments to the user experience data mayinclude adjustments such as, but not limited to, changing the colors,fonts, shapes, presentation, and/or layout of the information contentdata presented to the current user.

For example, in one embodiment, as discussed above, the application is adigital therapeutics application, and the current user is a patient whohas been diagnosed with a medical condition. Many patients experience agreat deal of anxiety related to their medical conditions. If thepredictive mental state data indicates that a user may be suffering fromanxiety, or may otherwise be in psychological distress, a decision maybe made that the current user would benefit from assistance, or fromadjustments designed to reduce the current user's anxiety level.

As one specific illustrative example, if a determination is made thatthe current user is mildly anxious, minor actions may be taken to reducethe current user's anxiety level, such as adjusting the content and/orpresentation of the information that is being provided to the currentuser through the user interface. As one simplified illustrative example,cool colors such as blue and violet are known to produce calmingeffects, and rounder, softer shapes are also associated with calmingeffects. So in this situation, the user experience content data may bemodified so that the content is presented to the user with a blue/violetcolor scheme, and the graphical user elements may be changed to includerounder and softer shapes. As another specific illustrative example, ifa determination is made that the current user is extremely anxious, thenmore extreme actions may be taken, such as notifying one or more medicalprofessionals associated with the user through a notification system ofthe application, or some other form of personal intervention from one ormore medical professionals associated with the current user.

In various embodiments several additional types of actions may beappropriate specifically when dealing with users who have been diagnosedwith one or more medical conditions, such as, but not limited to: askingthe user for input and/or response data; alerting the user; alerting oneor more of the user's mental health or medical professionals; makingnotes in, adding data to, or highlighting the user's electronic file;making a specialist referral; recommending support contacts to the user;prescribing additional appointments, treatments, actions, ormedications; calling emergency response or intervention professionals;notifying emergency contacts, relatives, or caregivers, etc.

In one embodiment, once one or more actions are taken based, at least inpart, on current user mental state prediction data at 520, process flowproceeds to END 522 and the process 500 for remotely identifying orpredicting the psychological state of application users based on machinelearning-based analysis and processing is exited to await new dataand/or instructions.

FIG. 6 is a block diagram of a production environment 600 for remotelyidentifying or predicting the psychological state of application usersbased on machine learning-based analysis and processing in accordancewith a third embodiment.

In one embodiment, production environment 600 includes user computingenvironments 602, current user computing environment 606, and serviceprovider computing environment 610. User computing environments 602 andcurrent user computing environment 606 further comprise user computingsystems 604 and current user computing system 608, respectively. Thecomputing environments 602, 606, and 610 are communicatively coupled toeach other with one or more communication networks 616.

In one embodiment, service provider computing environment 610 includesprocessor 612, physical memory 614, and application environment 618.Processor 612 and physical memory 614 coordinate the operation andinteraction of the data and data processing modules associated withapplication environment 618. In one embodiment, application environment618 includes user interface 620, which is provided to user computingsystems 604 and current user computing system 608 through the one ormore communication networks 616.

In one embodiment, application environment 618 further includes userinteraction data generation module 626, user mental state acquisitionmodule 628, user data correlation module 636, machine learning trainingmodule 640, action determination module 648, and action execution module650, each of which will be discussed in further detail below.

Additionally, in one embodiment, application environment 618 includesinformation content data 622, user experience data 624, user interactiondata 632, user mental state data 634, correlated user interaction andmental state data 638, current user interaction data 644, trainedmachine learning based mental state prediction models 642, and currentuser mental state prediction data 646, each of which will be discussedin further detail below. In some embodiments, user interaction data 632,user mental state data 634, correlated user interaction and mental statedata 638, and current user interaction data 644 may be stored in userdatabase 630, which includes data associated with one or more users ofapplication environment 618.

In one embodiment, user computing systems 604 of user computingenvironments 602, which are associated with one or more users ofapplication environment 618, are provided with a user interface 620,which allows the one or more users to receive output from theapplication environment 618, as well as to provide input to theapplication environment 618, through the one or more communicationnetworks 616.

As discussed above, in various embodiments, the application environment618 may be any type of application environment that is capable ofproviding a user interface and content/information to a user, including,but not limited to, a desktop computing system application, a mobilecomputing system application, a virtual reality computing systemapplication, an application provided by an Internet of Things (IoT)device, or any combination thereof. Further, in various embodiments, theuser interface 620 may include any combination of a graphical userinterface, an audio-based user interface, a touch-based user interface,or any other type of user interface currently known to those of skill inthe art, or any other type of user interface that may be developed afterthe time of filing.

In one embodiment, user computing systems 604 of user computingenvironments 602, which are associated with one or more users ofapplication environment 618, are provided with information content data622 and user experience data 624 through the user interface 620.

In various embodiments, the information content data 622 provided to theone or more users through the user interface 620 includes, but is notlimited to, textual information, audio information, graphicalinformation, image information, video information, and/or anycombination thereof. In one embodiment, the information content data 622is provided to the one or more users in such a way that allows the oneor more users to interact with the information content data 622.

In various embodiments, the user experience data 624 includes, but isnot limited to, colors and fonts used to present the information contentdata 622 to a user, the various shapes of graphical user interfaceelements, the layout or ordering of the information content data 622,and/or the sound effects, music, or other audio elements that mayaccompany the presentation of or interaction with the informationcontent data 622.

In one embodiment, once the one or more users are provided withinformation content data 622 and user experience data 624 through theuser interface 620, the interactions of the one or more users with theinformation content data 622 are monitored by user interaction datageneration module 626 through collection of user input data receivedthrough the user interface 620. The user input data collected by userinteraction data generation module 626 may include, but is not limitedto, data associated with click-stream input, textual input, touch input,gesture input, audio input, image input, video input, accelerometerinput, and/or physiological input. In one embodiment, once the userinput data is collected by user interaction data generation module 626,the user input data from each of the one or more users is processed andaggregated by user interaction data generation module 626 to generateuser interaction data 632.

In various embodiments, user interaction data may include data such as,but not limited to, the number of times a user accesses the applicationenvironment 618, the length of time a user spends engaging with theapplication environment 618, how long a user has had access to theapplication environment 618, the type of information content data 622that a user engages with the most while using the applicationenvironment 618, whether or not a user utilizes advanced inputmechanisms that may be provided by user interface 620, the type of inputmechanisms most preferred by a user, the speed at which a user interactswith the information content data 622 presented through the userinterface 620, and the level of comprehension a user has of theinformation content data 622 presented through the user interface 620.

In one embodiment, once user interaction data 632 has been generated byuser interaction data generation module 626, user mental state data 634is obtained for each of the one or more users by user mental stateacquisition module 628, and the user interaction data 632 for each ofthe one or more users is correlated with the user mental state data 634corresponding to each of the one or more users. In one embodiment theuser mental state data 634 is obtained from the one or more users byuser mental state acquisition module 628, before, after, or duringgeneration of the user interaction data 632 by user interaction datageneration module 626. In various embodiments, the user mental stateacquisition module 628 acquires the user mental state data 634 throughvarious mechanisms, such as, but not limited to, interviewing the user,consulting with a third party, such as a medical professional associatedwith the user, and/or obtaining and analyzing one or more filesassociated with a user.

Once user mental state data 634 is obtained for one or more users byuser mental state acquisition module 628, the user mental state data 634for each user is correlated with the associated user interaction data632 by user data correlation module 636. The correlated user mentalstate data 634 and user interaction data 632 for each of the one or moreusers is then aggregated by user data correlation module 636 to generatecorrelated user interaction and mental state data 638.

In one embodiment, once the correlated user interaction and mental statedata 638 is generated by user data correlation module 636, thecorrelated user interaction and mental state data 638 is used astraining data by machine learning training module 640 to create one ormore trained machine learning based mental state prediction models 642.

In various embodiments, and largely depending on the machine learningbased models used, the correlated user interaction and mental state data638 is processed by machine learning training module 640, using variousmethods know in the machine learning arts to identify elements andvectorize the correlated user interaction and mental state data 638. Asa specific illustrative example, in a case where the machine leaningbased model is a supervised model, the user interaction data 632 can beanalyzed and processed to identify individual elements found to beindicative of a user's mental state. These individual elements are thenused to create user interaction data vectors in multidimensional spacewhich are, in turn, used as input data for training one or more machinelearning models. The user mental state data 634 that correlates with theuser interaction data vector associated with that user is then used as alabel for the resulting vector. In various embodiments, this process isrepeated by machine learning training module 640 for the userinteraction data 632 and user mental state data 634 received from eachof the one or more users, such that multiple, often millions, ofcorrelated pairs of user interaction data vectors and mental state dataare used to train one or more machine learning based models.Consequently, this process results in the creation of one or moretrained machine learning based mental state prediction models 642.

In one embodiment, once the correlated user interaction and mental statedata 638 is used as training data by machine learning training module640 to create one or more trained machine learning based mental stateprediction models 642, current user computing system 608 of usercomputing environment 606, which is associated with a current user ofapplication environment 618, is provided with information content data622 and user experience data 624 through the user interface 620.

In one embodiment, once the current user is provided with informationcontent data 622 and user experience data 624 through the user interface620, the interactions of the current user with the information contentdata 622 are monitored by user interaction data generation module 626through collection of user input data received through the userinterface 620. The user input data collected by user interaction datageneration module 626 may include, but is not limited to, dataassociated with click-stream input, textual input, touch input, gestureinput, audio input, image input, video input, accelerometer input,and/or physiological input. In one embodiment, once the current userinput data is collected by user interaction data generation module 626,the current user input data is processed and aggregated by userinteraction data generation module 626 to generate current userinteraction data 644.

In one embodiment, once current user interaction data 644 has beengenerated by user interaction data generation module 626, the currentuser interaction data 644 is provided to the one or more trained machinelearning based mental state prediction models 642 to generate currentuser mental state prediction data 646.

In one embodiment, the current user interaction data 644 is vectorizedto generate one or more user interaction data vectors. The one or moreuser interaction data vectors associated with the current user are thenprovided as input data to the one or more trained machine learning basedmental state prediction models 642, resulting in the generation ofcurrent user mental state prediction data 646. In one embodiment, thecurrent user mental state prediction data 646 comprises one or moreprobability scores, which indicate the probability that the current useris in one or more particular mental states.

In one embodiment, once current user mental state prediction data 646 isgenerated by the one or more trained machine learning based mental stateprediction models 642, one or more actions are taken based, at least inpart, on the current user mental state prediction data 646.

In one embodiment, the one or more actions to be taken may be determinedby action determination module 648 based on the current user mentalstate prediction data 646. For example, if current user mental stateprediction data 646 indicates that the current user is mildly anxious,then action determination module 648 may determine that actions shouldbe taken to make slight adjustments to the information content data 622and/or the user experience data 624 that is presented to the currentuser through the user interface 620. On the other hand, if the currentuser mental state prediction data 646 indicates that the current user isseverely anxious, then action determination module 648 may determinethat actions should be taken to make major adjustments to theinformation content data 622 and/or the user experience data 624 that ispresented to the current user through the user interface 620. In otherembodiments, action determination module 648 may determine that moreextreme actions should be taken. For example, if the current user mentalstate prediction data 646 indicates that the current user is severelyanxious, then action determination module 648 may determine that actionssuch as emergency notifications and personal intervention areappropriate.

In various embodiments, once action determination module 648 determinesactions to be taken, control proceeds to action execution module 650 forexecution of the determined actions. Action execution may include, forexample, selecting and providing different information content data 622or user experience data 624 that is more appropriate for the currentuser's psychological state, contacting the user through any userapproved contact means, and/or contacting a user's trusted third partyon behalf of the user.

The embodiments disclosed above provide an effective and efficienttechnical solution to the technical problem of remotely identifying andmonitoring changes or anomalies in the psychological state ofapplication users. One specific practical application of the disclosedembodiments is that of remotely identifying and monitoring changes oranomalies in the psychological state of patients who have been diagnosedwith one or more medical conditions. In the disclosed embodiments, apatient diagnosed with one or more medical conditions is prescribedaccess to a digital therapeutics application, which is designed toprovide guided care to the patient in a variety of ways. Through thedigital therapeutics application, the patient may be provided withinformation, such as information relating to one or more of thepatient's medical conditions, as well as current and potentialmedications and/or treatments. In addition, the digital therapeuticsapplication disclosed herein further provides the patient withinteractive content, which allows for the collection of data related toaspects of the patient's interaction with the provided content. Thecollected interaction data is then analyzed to identify and monitorchanges or anomalies in the psychological state of the patient. Uponidentification of changes or anomalies in the patient's psychologicalstate, one or more actions are taken to assist the patient.

Consequently, the embodiments disclosed herein are not an abstract idea,and are well-suited to a wide variety of practical applications.Further, many of the embodiments disclosed herein require processing andanalysis of billions of data points and combinations of data points, andthus, the technical solution disclosed herein cannot be implementedsolely by mental steps or pen and paper, is not an abstract idea, andis, in fact, directed to providing technical solutions to long-standingtechnical problems associated with remotely monitoring the psychologicalstate of application users.

Additionally, the disclosed method and system for remotely monitoringthe psychological state of application users requires a specific processcomprising the aggregation and detailed analysis of large quantities ofuser input and interaction data, and as such, does not encompass,embody, or preclude other forms of innovation in the area ofpsychological monitoring. Further, the disclosed embodiments of systemsand methods for remotely monitoring the psychological state ofapplication users are not abstract ideas for at least several reasons.

First, effectively and efficiently monitoring the psychological state ofapplication users remotely is not an abstract idea because it is notmerely an idea in and of itself. For example, the process cannot beperformed mentally or using pen and paper, as it is not possible for thehuman mind to identify, process, and analyze all possible combinationsof user inputs, user interactions, and user psychological states, evenwith pen and paper to assist the human mind and even with unlimitedtime.

Second, effectively and efficiently monitoring the psychological stateof application users remotely is not a fundamental economic practice(e.g., is not merely creating a contractual relationship, hedging,mitigating a settlement risk, etc.).

Third, effectively and efficiently monitoring the psychological state ofapplication users remotely is not merely a method of organizing humanactivity (e.g., managing a game of bingo). Rather, in the disclosedembodiments, the method and system for effectively and efficientlymonitoring the psychological state of application users remotelyprovides a tool that significantly improves the fields of medical andmental health care. Through the disclosed embodiments, patients areprovided with unique and personalized remote assistance, treatment, andcare. As such, the method and system disclosed herein is not an abstractidea, and also serves to integrate the ideas disclosed herein intopractical applications of those ideas.

Fourth, although mathematics may be used to implement the embodimentsdisclosed herein, the systems and methods disclosed and claimed hereinare not abstract ideas because the disclosed systems and methods are notsimply a mathematical relationship/formula.

It should be noted that the language used in the specification has beenprincipally selected for readability, clarity, and instructionalpurposes, and may not have been selected to delineate or circumscribethe inventive subject matter. Accordingly, the disclosure of the presentinvention is intended to be illustrative, but not limiting, of the scopeof the invention, which is set forth in the claims below.

The present invention has been described in particular detail withrespect to specific possible embodiments. Those of skill in the art willappreciate that the invention may be practiced in other embodiments. Forexample, the nomenclature used for components, capitalization ofcomponent designations and terms, the attributes, data structures, orany other programming or structural aspect is not significant,mandatory, or limiting, and the mechanisms that implement the inventionor its features can have various different names, formats, or protocols.Further, the system or functionality of the invention may be implementedvia various combinations of software and hardware, as described, orentirely in hardware elements. Also, particular divisions offunctionality between the various components described herein are merelyexemplary, and not mandatory or significant. Consequently, functionsperformed by a single component may, in other embodiments, be performedby multiple components, and functions performed by multiple componentsmay, in other embodiments, be performed by a single component.

Some portions of the above description present the features of thepresent invention in terms of algorithms and symbolic representations ofoperations, or algorithm-like representations, of operations oninformation/data. These algorithmic or algorithm-like descriptions andrepresentations are the means used by those of skill in the art to mosteffectively and efficiently convey the substance of their work to othersof skill in the art. These operations, while described functionally orlogically, are understood to be implemented by computer programs orcomputing systems. Furthermore, it has also proven convenient at timesto refer to these arrangements of operations as steps or modules or byfunctional names, without loss of generality.

In addition, the operations shown in the figures, or as discussedherein, are identified using a particular nomenclature for ease ofdescription and understanding, but other nomenclature is often used inthe art to identify equivalent operations.

Therefore, numerous variations, whether explicitly provided for by thespecification or implied by the specification or not, may be implementedby one of skill in the art in view of this disclosure.

What is claimed is:
 1. A computing system implemented method comprising:providing a digital therapeutic system to one or more users; identifyingone or more physiological conditions of the one or more users;providing, by one or more processors of the digital therapeutic system,the one or more users with therapeutic content through a user interfaceof the digital therapeutic system, wherein the therapeutic contentprovides treatment for one or more of the physiological conditions ofthe one or more users; remotely monitoring, by one or more processors ofthe digital therapeutic system, interactions of the one or more userswith the therapeutic content provided through the user interface togenerate user interaction data representing the interactions of the oneor more users with the therapeutic content during a defined period oftime; processing, by one or more processors of the digital therapeuticsystem, the user interaction data for the one or more users to generateaverage user interaction data representing average measurementsassociated with interactions between the one or more users and thetherapeutic content provided through the user interface; providing thedigital therapeutic system to a current patient user; identifying one ormore physiological conditions of the current patient user; digitallygenerating, by one or more processors of the digital therapeutic system,current therapeutic content, wherein the current therapeutic contentprovides the current patient user with treatment for one or more of thecurrent patient user's physiological conditions; providing, by one ormore processors of the digital therapeutic system, the current patientuser with the digitally generated current therapeutic content throughthe user interface of the digital therapeutic system during a currentsession of the current patient user interacting with the digitaltherapeutic system; remotely monitoring, by one or more processors ofthe digital therapeutic system, interactions of the current patient userwith the current therapeutic content provided through the user interfaceto generate current patient user interaction data representing theinteractions of the current patient user with the current therapeuticcontent during the current session; defining one or more threshold userinteraction differentials representing one or more maximum allowablevariations between a current patient user's current user interactiondata and average user interaction data associated with one or more usersother than the current patient user; generating threshold userinteraction differential data representing the one or more thresholduser interaction differentials; generating, by one or more processors ofthe digital therapeutic system, during the current session, currentpatient user interaction differential data representing one or moredifferentials between the current patient user interaction data and theaverage user interaction data; and if a current patient user interactiondifferential represented by the current patient user interactiondifferential data is greater than one or more of the threshold userinteraction differentials represented by the threshold user interactiondifferential data: determining that one or more anomalies exist in thecurrent patient user's current mental state; digitally generating, byone or more processors of the digital therapeutic system, during thecurrent session, dynamically modified current therapeutic content basedat least in part on the one or more anomalies in the current patientuser's current mental state; and automatically providing, using one ormore processors of the digital therapeutic system, during the currentsession, the current patient user with the dynamically modified currenttherapeutic content through the user interface of the digitaltherapeutic system.
 2. The computing system implemented method of claim1 wherein processing the user interaction data for the one or more usersfurther includes: for each of the one or more users, obtaining mentalstate data for the user during the defined period of time in which theuser is interacting with the therapeutic content; for each of the one ormore users, correlating each user's user interaction data with thatuser's mental state data; collecting and processing the correlated userinteraction data and user mental state data for each of the one or moreusers to generate machine learning-based mental state prediction modeltraining data; and providing the machine learning-based mental stateprediction model training data to one or more machine learning-basedprediction models to generate one or more trained machine learning-basedmental state prediction models.
 3. The computing system implementedmethod of claim 2 wherein determining that one or more anomalies existin the current patient user's current mental state includes: providingthe current patient user interaction data to the one or more trainedmachine learning-based mental state prediction models; and receivingcurrent patient user mental state prediction data from the one or moretrained machine learning-based mental state prediction models.
 4. Thecomputing system implemented method of claim 1 wherein the therapeuticcontent provided to a user through the user interface of the digitaltherapeutic system includes one or more of: textual information relatedto the user's one or more physiological conditions; audio informationrelated to the user's one or more physiological conditions; graphicalinformation related to the user's one or more physiological conditions.image information related to the user's one or more physiologicalconditions; and video information related to the user's one or morephysiological conditions.
 5. The computing system implemented method ofclaim 1 wherein monitoring interactions of a user includes monitoringone or more of: click-stream input; textual input; touch input; gestureinput; audio input; image input; video input; accelerometer input; andphysiological input.
 6. The computing system implemented method of claim1 wherein monitoring interactions of a user includes one or more of:monitoring a speed at which the user interacts with the therapeuticcontent provided through the user interface; and monitoring the user'scomprehension of the therapeutic content provided through the userinterface.
 7. The computing system implemented method of claim 6 whereinthe speed at which the user interacts with the therapeutic contentprovided through the user interface is measured by monitoring one ormore of: the speed at which the user scrolls through the therapeuticcontent provided through the user interface; the speed at which the userclicks through the therapeutic content provided through the userinterface; and the speed at which the user enters text through the userinterface.
 8. The computing system implemented method of claim 6 whereinthe user's comprehension of the therapeutic content provided through theuser interface is measured by one or more of: presenting the user withquestions related to the provided therapeutic content; and determining apercentage of the provided therapeutic content that the user hasinteracted with.
 9. The computing system implemented method of claim 1wherein, upon determining that one or more anomalies exist in thecurrent patient user's current mental state, one or more actions aretaken, including one or more of: adjusting presentation of thetherapeutic content provided to the current patient user; adjusting thetherapeutic content provided to the current patient user; requestinginformation from the current patient user; contacting the currentpatient user directly; contacting a third party on the current patientuser's behalf; adding a note to the current patient user's file forreview by a third party; and flagging the current patient user's filefor attention by a third party.
 10. A computing system implementedmethod comprising: providing a digital therapeutic system to a patientuser; identifying one or more physiological conditions of the patientuser; providing, by one or more processors of the digital therapeuticsystem, the patient user with therapeutic content through a userinterface of the digital therapeutic system, wherein the therapeuticcontent provides treatment for one or more of the physiologicalconditions of the patient user; remotely monitoring, by one or moreprocessors of the digital therapeutic system, interactions of thepatient user with the therapeutic content provided through the userinterface to generate historical user interaction data representing thehistorical interactions of the patient user with the therapeutic contentduring a defined period of time; processing, by one or more processorsof the digital therapeutic system, the historical user interaction datafor the patient user to generate baseline user interaction datarepresenting baseline measurements associated with interactions betweenthe patient user and the therapeutic content provided through the userinterface; digitally generating, by one or more processors of thedigital therapeutic system, current therapeutic content, wherein thecurrent therapeutic content provides the patient user with treatment forone or more of the patient user's physiological conditions; providing,by one or more processors of the digital therapeutic system, the patientuser with the digitally generated current therapeutic content throughthe user interface of the digital therapeutic system during a currentsession of the patient user interacting with the digital therapeuticsystem; remotely monitoring, by one or more processors of the digitaltherapeutic system, interactions of the patient user with the currenttherapeutic content provided through the user interface to generatecurrent user interaction data representing the interactions of thepatient user with the current therapeutic content during the currentsession; defining one or more threshold user interaction differentialsrepresenting one or more maximum allowable variations between a user'scurrent user interaction data and a user's baseline user interactiondata; generating threshold user interaction differential datarepresenting the one or more threshold user interaction differentials;generating, by one or more processors of the digital therapeutic system,during the current session, current user interaction differential datarepresenting one or more differentials between the patient user'scurrent user interaction data and the patient user's baseline userinteraction data; if a current user interaction differential representedby the current user interaction differential data is greater than one ormore of the threshold user interaction differentials represented by thethreshold user interaction differential data: determining that one ormore anomalies exist in the patient user's current mental state;digitally generating, by one or more processors of the digitaltherapeutic system, during the current session, dynamically modifiedcurrent therapeutic content based at least in part on the one or moreanomalies in the patient user's current mental state; and automaticallyproviding, using one or more processors of the digital therapeuticsystem, during the current session, the patient user with thedynamically modified current therapeutic content through the userinterface of the digital therapeutic system.
 11. The computing systemimplemented method of claim 10 wherein processing the historical userinteraction data for the patient user further includes: obtaining mentalstate data for the patient user during the defined period of time inwhich the patient user is interacting with the therapeutic content;correlating the patient user's historical interaction data with thepatient user's mental state data; collecting and processing thecorrelated historical user interaction data and mental state data forthe patient user to generate machine learning-based mental stateprediction model training data; and providing the machine learning-basedmental state prediction model training data to one or more machinelearning-based prediction models to generate one or more trained machinelearning-based mental state prediction models.
 12. The computing systemimplemented method of claim 11 wherein determining that one or moreanomalies exist in the patient user's current mental state includes:providing the patient user's current user interaction data to the one ormore trained machine learning-based mental state prediction models; andreceiving current patient user mental state prediction data from the oneor more trained machine learning-based mental state prediction models.13. The computing system implemented method of claim 10 wherein thetherapeutic content provided to the patient user through the userinterface of the digital therapeutic system includes one or more of:textual information related to the patient user's one or morephysiological conditions; audio information related to the patientuser's one or more physiological conditions; graphical informationrelated to the patient user's one or more physiological conditions.image information related to the patient user's one or morephysiological conditions; and video information related to the patientuser's one or more physiological conditions.
 14. The computing systemimplemented method of claim 10 wherein monitoring interactions of thepatient user includes remotely monitoring one or more of: click-streaminput; textual input; touch input; gesture input; audio input; imageinput; video input; accelerometer input; and physiological input. 15.The computing system implemented method of claim 10 wherein monitoringinteractions of the patient user includes one or more of: monitoring aspeed at which the patient user interacts with the therapeutic contentprovided through the user interface; and monitoring the patient user'scomprehension of the therapeutic content provided through the userinterface.
 16. The computing system implemented method of claim 15wherein the speed at which the patient user interacts with thetherapeutic content provided through the user interface is measured bymonitoring one or more of: the speed at which the patient user scrollsthrough the therapeutic content provided through the user interface; thespeed at which the patient user clicks through the therapeutic contentprovided through the user interface; and the speed at which the patientuser enters text through the user interface.
 17. The computing systemimplemented method of claim 15 wherein the patient user's comprehensionof the therapeutic content provided through the user interface ismeasured by one or more of: presenting the patient user with questionsrelated to the provided therapeutic content; and determining apercentage of the provided therapeutic content that the patient user hasinteracted with.
 18. The computing system implemented method of claim 10wherein, upon determining that one or more anomalies exist in thepatient user's current mental state, one or more actions are taken,including one or more of: adjusting presentation of the currenttherapeutic content provided to the patient user; adjusting the currenttherapeutic content provided to the patient user; requesting informationfrom the patient user; contacting the patient user directly; contactinga third party on the patient user's behalf; adding a note to the patientuser's file for review by a third party; and flagging the patient user'sfile for attention by a third party.
 19. A computing system implementedmethod comprising: obtaining average user interaction data representingaverage measurements associated with interactions between one or moreusers and therapeutic content provided through a user interface of adigital therapeutic system; providing the digital therapeutic system toa current patient user; identifying one or more physiological conditionsof the current patient user; digitally generating, by one or moreprocessors of the digital therapeutic system, current therapeuticcontent, wherein the current therapeutic content provides the currentpatient user with treatment for one or more of the current patientuser's physiological conditions; providing, by one or more processors ofthe digital therapeutic system, the current patient user with thedigitally generated current therapeutic content through the userinterface of the digital therapeutic system during a current session ofthe current patient user interacting with the digital therapeuticsystem; remotely monitoring, by one or more processors of the digitaltherapeutic system, interactions of the current patient user with thecurrent therapeutic content provided through the user interface togenerate current patient user interaction data representing theinteractions of the current patient user with the current therapeuticcontent during the current session; defining one or more threshold userinteraction differentials representing one or more maximum allowablevariations between a current patient user's current user interactiondata and average user interaction data associated with one or more usersother than the current patient user; generating threshold userinteraction differential data representing the one or more thresholduser interaction differentials; generating, by one or more processors ofthe digital therapeutic system, during the current session, currentpatient user interaction differential data representing one or moredifferentials between the current patient user interaction data and theaverage user interaction data; if a current patient user interactiondifferential represented by the current patient user interactiondifferential data is greater than one or more of the threshold userinteraction differentials represented by the threshold user interactiondifferential data: determining that one or more anomalies exist in thecurrent patient user's current mental state; digitally generating, byone or more processors of the digital therapeutic system, during thecurrent session, dynamically modified current therapeutic content basedat least in part on the one or more anomalies in the current patientuser's current mental state; and automatically providing, using one ormore processors of the digital therapeutic system, during the currentsession, the current patient user with the dynamically modified currenttherapeutic content through the user interface of the digitaltherapeutic system.
 20. A computing system implemented methodcomprising: obtaining baseline user interaction data representingbaseline measurements associated with interactions between a patientuser and therapeutic content provided through a user interface of adigital therapeutic system; identifying one or more physiologicalconditions of the patient user; digitally generating, by one or moreprocessors of the digital therapeutic system, current therapeuticcontent, wherein the current therapeutic content provides the patientuser with treatment for one or more of the patient user's physiologicalconditions; providing, by one or more processors of the digitaltherapeutic system, the patient user with the digitally generatedcurrent therapeutic content through the user interface of the digitaltherapeutic system during a current session of the patient userinteracting with the digital therapeutic system; remotely monitoring, byone or more processors of the digital therapeutic system, interactionsof the patient user with the current therapeutic content providedthrough the user interface to generate current patient user interactiondata representing the interactions of the patient user with the currenttherapeutic content during the current session; defining one or morethreshold user interaction differentials representing one or moremaximum allowable variations between a user's current user interactiondata and a user's baseline user interaction data; generating thresholduser interaction differential data representing the one or morethreshold user interaction differentials; generating, by one or moreprocessors of the digital therapeutic system, during the currentsession, current user interaction differential data representing one ormore differentials between the patient user's current user interactiondata and the patient user's baseline user interaction data; if a currentuser interaction differential represented by the current userinteraction differential data is greater than one or more of thethreshold user interaction differentials represented by the thresholduser interaction differential data: determining that one or moreanomalies exist in the patient user's current mental state; digitallygenerating, by one or more processors of the digital therapeutic system,during the current session, dynamically modified current therapeuticcontent based at least in part on the one or more anomalies in thepatient user's current mental state; and automatically providing, usingone or more processors of the digital therapeutic system, during thecurrent session, the patient user with the dynamically modified currenttherapeutic content through the user interface of the digitaltherapeutic system.